Chris R. Reid

Animal groups need to achieve and maintain consensus to minimize conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behaviour, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here, we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a ‘wisdom-of-the-crowd’ strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.

Animal collective behavior produces group‐level patterns that emerge from individual‐level interactions among members. Such sophisticated behavioral coordination can be observed across various taxa, indicating evolutionary convergence. Among the most striking examples are two social insect lineages: ants and termites. Ants and termites evolved socially independently, inhabit terrestrial and subterranean environments, and face a similar collective task of maintaining their colonies with many individuals. This results in the convergent evolution of collective phenomena, such as constructing complex nest structures or movement coordination based on chemical and tactile cues. However, although the striking similarity attracts the most attention, little research has focused on how ants and termites differ in collective behaviors at individual and group levels. Here, we review the similarities and distinctions of collective behaviors in ants and termites. Even with similarities in group‐level patterns, rich diversity exists in mechanisms of behavioral coordination and the functions of collective patterns. Comparative analysis of collective behavior is challenging, but recent advances in automatic movement tracking methodologies have greatly expanded the potential for generating further insights. Finally, we conclude that comparative collective behavioral analysis can enhance our ability to understand biodiversity and provide alternative solutions for collective problem‐solving in many fields.

Animal groups need to achieve and maintain consensus to minimise conflict among individuals and prevent group fragmentation. An excellent example of a consensus challenge is cooperative transport, where multiple individuals cooperate to move a large item together. This behavior, regularly displayed by ants and humans only, requires individuals to agree on which direction to move in. Unlike humans, ants cannot use verbal communication but most likely rely on private information and/or mechanical forces sensed through the carried item to coordinate their behaviour. Here we investigated how groups of weaver ants achieve consensus during cooperative transport using a tethered-object protocol, where ants had to transport a prey item that was tethered in place with a thin string. This protocol allows the decoupling of the movement of informed ants from that of uninformed individuals. We showed that weaver ants pool together the opinions of all group members to increase their navigational accuracy. We confirmed this result using a symmetry-breaking task, in which we challenged ants with navigating an open-ended corridor. Weaver ants are the first reported ant species to use a ‘wisdom of the crowd’ strategy for cooperative transport, demonstrating that consensus mechanisms may differ according to the ecology of each species.

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.

Tracking individuals is a vital part of many experiments conducted to understand collective behaviour. Ants are the paradigmatic model system for such experiments but their lack of individually distinguishing visual features and their high colony densities make it extremely difficult to perform reliable racking automatically. Additionally, the wide diversity of their species' appearances makes a generalized approach even harder. In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss. In addition to this novel domain-adaptive tracking framework, we present a new dataset and a benchmark for the ant tracking problem. The dataset contains 57 video sequences with full trajectory annotation, including 30k frames captured from two different ant species moving on different background patterns. It comprises 33 and 24 sequences for source and target domains, respectively. We compare our proposed framework against other domain-adaptive and non-domain-adaptive multi-object tracking baselines using this dataset and show that incorporating domain adaptation at multiple levels of the tracking pipeline yields significant improvements. The code and the dataset are available at https://github.com/chamathabeysinghe/da-tracker.

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 antOecophylla smaragdinaform 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.

Tracking individuals is a vital part of many experiments conducted to understand collective behaviour. Ants are the paradigmatic model system for such experiments but their lack of individually distinguishing visual features and their high colony densities make it extremely difficult to perform reliable tracking automatically. Additionally, the wide diversity of their species' appearances makes a generalized approach even harder. In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss. In addition to this novel domain-adaptive tracking framework, we present a new dataset and a benchmark for the ant tracking problem. The dataset contains 57 video sequences with full trajectory annotation, including 30k frames captured from two different ant species moving on different background patterns. It comprises 33 and 24 sequences for source and target domains, respectively. We compare our proposed framework against other domain-adaptive and non-domain-adaptive multi-object tracking baselines using this dataset and show that incorporating domain adaptation at multiple levels of the tracking pipeline yields significant improvements. The code and the dataset are available at https://github.com/chamathabeysinghe/da-tracker.

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 mouldPhysarum 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 ofPhysarumand animal cognition research.

Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants col