Group-living animals sleep together, yet most research treats sleep as an individual process. Here, we argue that social interactions during the sleep period contribute in important, but largely overlooked, ways to animal groups\' social dynamics, while patterns of social interaction and the structure of social connections within animal groups play important, but poorly understood, roles in shaping sleep behavior. Leveraging field-appropriate methods, such as direct and video-based observation, and increasingly common on-animal motion sensors (e.g., accelerometers), behavioral indicators can be tracked to measure sleep in multiple individuals in a group of animals simultaneously. Sleep proximity networks and sleep timing networks can then be used to investigate the collective dynamics of sleep in wild group-living animals.

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

The world is grappling with emerging, urgent, large-scale problems, such as climate change, pollution, biodiversity loss, and pandemics, which demand immediate and coordinated action. Social processes like conformity and social norms can either help maintain behaviors (e.g. cooperation in groups) or drive rapid societal change (e.g. rapid rooftop solar uptake), even without comprehensive policy measures. While the role of individual heterogeneity in such processes is well studied, there is limited work on the expression of individuals\' preferences and the role of anticonformists-individuals who value acting differently from others-especially in dynamic environments. We introduce anticonformists into a game-theoretical collective decision-making framework that includes a complex network of agents with heterogeneous preferences about two alternative options. We study how anticonformists\' presence changes the population\'s ability to express evolving personal preferences. We find that anticonformists facilitate the expression of preferences, even when they diverge from prevailing norms, breaking the \"spiral of silence\" whereby individuals do not act on their preferences when they believe others disapprove. Centrally placed anticonformists reduce by five-fold the number of anticonformists needed for a population to express its preferences. In dynamic environments where a previously unpopular choice becomes preferred, anticonformists catalyze social tipping and reduce the \"cultural lag,\" even beyond the role of committed minorities-that is, individuals with a commitment to a specific cause. This research highlights the role of dissenting voices in shaping collective behavior, including their potential to catalyze the adoption of new technologies as they become favorable and to enrich democracy by facilitating the expression of views.

The collective behavior of animal groups emerges from the interactions among individuals. These social interactions produce the coordinated movements of bird flocks and fish schools, but little is known about their developmental emergence and neurobiological foundations. By characterizing the visually based schooling behavior of the micro glassfish Danionella cerebrum, we found that social development progresses sequentially, with animals first acquiring the ability to aggregate, followed by postural alignment with social partners. This social maturation was accompanied by the development of neural populations in the midbrain that were preferentially driven by visual stimuli that resemble the shape and movements of schooling fish. Furthermore, social isolation over the course of development impaired both schooling behavior and the neural encoding of social motion in adults. This work demonstrates that neural populations selective for the form and motion of conspecifics emerge with the experience-dependent development of collective movement.

The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed-accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.

Active fluids composed of constituents that are constantly driven away from thermal equilibrium can support spontaneous currents and can be engineered to have unconventional transport properties. Here, we report the emergence of (meta)stable traveling bands in computer simulations of aligning circle swimmers. These bands are different from polar flocks and, through coupling phase with mass transport, induce a bulk particle current with a component perpendicular to the propagation direction, thus giving rise to a collective Hall (or Magnus) effect. Traveling bands require sufficiently small orbits and undergo a discontinuous transition into a synchronized state with transient polar clusters for large orbital radii. Within a minimal hydrodynamic theory, we show that the bands can be understood as nondispersive soliton solutions fully accounting for the numerically observed properties.

Social information is predicted to enhance the quality of animals\' migratory decisions in dynamic ecosystems, but the relative benefits of social information in the long-range movements of marine megafauna are unknown. In particular, whether and how migrants use nonlocal information gained through social communication at the large spatial scale of oceanic ecosystems remains unclear. Here we test hypotheses about the cues underlying timing of blue whales\' breeding migration in the Northeast Pacific via individual-based models parameterized by empirical behavioral data. Comparing emergent patterns from individual-based models to individual and population-level empirical metrics of migration timing, we find that individual whales likely rely on both personal and social sources of information about forage availability in deciding when to depart from their vast and dynamic foraging habitat and initiate breeding migration. Empirical patterns of migratory phenology can only be reproduced by models in which individuals use long-distance social information about conspecifics\' behavioral state, which is known to be encoded in the patterning of their widely propagating songs. Further, social communication improves pre-migration seasonal foraging performance by over 60% relative to asocial movement mechanisms. Our results suggest that long-range communication enhances the perceptual ranges of migrating whales beyond that of any individual, resulting in increased foraging performance and more collective migration timing. These findings indicate the value of nonlocal social information in an oceanic migrant and suggest the importance of long-distance acoustic communication in the collective migration of wide-ranging marine megafauna.

Many bird species commonly aggregate in flocks for reasons ranging from predator defense to navigation. Available evidence suggests that certain types of flocks-the V and echelon formations of large birds-may provide a benefit that reduces the aerodynamic cost of flight, whereas cluster flocks typical of smaller birds may increase flight costs. However, metabolic flight costs have not been directly measured in any of these group flight contexts [Zhang and Lauder, J. Exp. Biol. 226, jeb245617 (2023)]. Here, we measured the energetic benefits of flight in small groups of two or three birds and the requirements for realizing those benefits, using metabolic energy expenditure and flight position measurements from European Starlings flying in a wind tunnel. The starlings continuously varied their relative position during flights but adopted a V formation motif on average, with a modal spanwise and streamwise spacing of [0.81, 0.91] wingspans. As measured via CO2 production, flight costs for follower birds were significantly reduced compared to their individual solo flight benchmarks. However, followers with more positional variability with respect to leaders did less well, even increasing their costs above solo flight. Thus, we directly demonstrate energetic costs and benefits for group flight followers in an experimental context amenable to further investigation of the underlying aerodynamics, wake interactions, and bird characteristics that produce these metabolic effects.

Understanding how animal collectives and societies form and function is a fundamental goal in animal biology. To date, research examining fish shoaling behavior has focused mostly on the general principles and ecological relevance of the phenomeon, while the ways in which physiological state (e.g., nutrition) affects collective behavior remain overlooked. Here, we investigated the shoaling behavior of common minnows (Phoxinus phoxinus) with three different nutritional states (control treatment: fasting for 24 h, fasting treatment: fasting for 7 days, and digestion treatment: 1 h after satiation feeding) across two ecological contexts (i.e., without and with food). No effects of either nutritional state or context were found on swimming speed, but the acceleration was greater in the digestion group than in the control group, with that in the fasting group being intermediate. Similar to change tendency in group length and group width of shoals, both interindividual distance and nearest neighbor distance were also greater in the fasting group than in the digestion group, suggesting that fasting and digestion may exert opposite driving forces on group cohesion. However, neither nutritional state nor context influenced the group area, group speed, group percent time moving, or group polarization. Both the foraging efficiency and the percentage of food items consumed by the fish shoals were greater in the fasting and control groups than in the digestion group. Our study suggested that one week of hunger and the energetically demanding stage of food digestion tend to have opposite influences on group shape, while the social foraging context does not influence the individual and group behavior of fish.

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulations to reality, using robotics to validate the modeling hypotheses. This challenge arises in bridging what we term the \'biomimicry gap\', which is caused by imperfect robotic replicas, communication cues and physics constraints not incorporated in the simulations, that may elicit unrealistic behavioral responses in animals. In this work, we used a biomimetic lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network (NN) model for generating biomimetic social interactions. Through experiments with a biohybrid pair comprising a fish and the robotic lure, a pair of real fish, and simulations of pairs of fish, we demonstrate that our biohybrid system generates social interactions mirroring those of genuine fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal deviation in real-world interactions compared to simulations and fish-only experiments, 2) our NN controls the robot efficiently in real-time, and 3) a comprehensive validation is crucial to bridge the biomimicry gap, ensuring realistic biohybrid systems.