Pranav Minasandra

Communication is fundamental to sociality and can shape interactions that are critical for individual fitness. Vocal communication is particularly important for many group-living animals, as it all...

For many group living species, vocal signals are a vital form of communication needed to coordinate social behaviours. Investigating these processes first requires a detailed description of the species' vocal repertoire. White-nosed coatis (Nasua narica), which forage and move together in forest habitats, are thought to rely on vocalisations to coordinate movements and maintain group cohesion. However, quantitative studies of their vocal repertoire are lacking. We examined the vocal repertoire of white-nosed coatis from wild populations in Panama and Arizona, USA, to gain a more comprehensive view of their calling behaviours. By combining traditional acoustic analyses with an unsupervised approach based on spectrogram structure, we characterised the diversity of calls in this species and described the temporal and structural features of their vocalisations. We identified 19 call types, with some of these calls emitted in multi-syllable call sequences or in fast succession. We found variability in call rates among group members, which may be driven by differences in social status within the group. In addition, our results indicate that white-nosed coatis likely have individually-recognisable vocalisations. This study provides a foundational description of the white-nosed coati vocal repertoire, laying the groundwork for future research on vocal communication in this species.

Real-time tracking and automated response systems are essential for standardising experiments, reducing observer bias, and improving reproducibility in studies of movement and behaviour. However, existing solutions face significant challenges: AI-based tracking systems require expensive hardware and impose computational delays, creating challenges for closed-loop experiments; existing real-time tracking tools lack standardised implementations for response delivery; and steep learning curves limit accessibility for users without programming or computer vision expertise. Here, we introduce TracktorLive, an open-source Python package designed to overcome these challenges through concurrency and a modular, ‘cassette’-based architecture. TracktorLive leverages traditional computer vision techniques to perform image-based object detection without the need for expensive hardware or deep learning. By parallelizing object tracking and response delivery into separate, concurrent server and client processes, the software minimizes frame processing time, enabling rapid, real-time analysis and response delivery. User-friendly ‘cassettes’—portable code snippets that can be copy-pasted into scripts—enable users with minimal programming experience to implement complex workflows for use in experiments and practical applications. We demonstrate TracktorLive’s utility through several applications, including microcontroller-based stimulus delivery for location-dependent manipulations; conditional video recording that activates only during events of interest; kinematic-based response triggering using real-time velocity computations; and multi-cassette experimental designs combining multiple functionalities. Detailed tutorials are provided to familiarize users with TracktorLive’s operation and functionality, and a growing library of cassettes supports diverse applications for processing both real-time and pre-recorded video. We validated the software by comparing its response timing to human experimenters in a stimulus delivery task involving two fish species, where TracktorLive demonstrated consistently higher accuracy and lower variability, particularly for fast-moving subjects. Beyond experimental biology, TracktorLive’s unique architecture and versatility could support many different applications in fields ranging from neuroscience to wildlife management. As an open-source software combining accessibility, modularity, and computational efficiency, TracktorLive can help democratize real-time tracking and automated response systems across disciplines.

Current models of collective animal movement explain several emergent phenomena. However, these models do not account for animals’ potential ability to forecast each others’ decision-making processes. The ability to predict and account for the behaviours of others is likely unique to animals, and the relevance of this ability in collective movement merits exploration. I granted anticipatory abilities to Hamilton’s selfish herd, a classic model of collective behaviour, using 14000 simulated populations and explored the consequences of this ability on collective movement patterns. I also simulated competitions between agents with and without anticipatory abilities, and showed that anticipation may confer a survival advantage.

Animal behavior can be decomposed into a sequence of discrete activity bouts over time. Analyzing the statistical structure of such behavioral sequences can provide insights into the drivers of behavioral decisions. Laboratory studies, predominantly in invertebrates, have suggested that behavioral sequences exhibit multiple timescales and long-range memory, but whether these results can be generalized to other taxa and to animals in natural settings remains unclear. By analyzing accelerometer-inferred predictions of behavioral states in three species of social mammals (meerkats, white-nosed coatis, and spotted hyenas) in the wild, we found surprisingly consistent structuring of behavioral sequences across all behavioral states, all individuals, and all study species. Behavioral bouts were characterized by decreasing hazard functions, wherein the longer a behavioral bout had progressed, the less likely it was to end within the next instant. The predictability of an animal’s future behavioral state as a function of its present state always decreased as a truncated power-law for predictions made farther into the future, with very similar estimates for the power law exponent across all species. Finally, the distributions of bout durations were also heavy-tailed. Why such shared structural principles emerge remains unknown, and we explore multiple plausible explanations, including environmental nonstationarity, behavioral self-reinforcement, and the hierarchical nature of behavior. The existence of highly consistent patterns in behavioral sequences across our study species suggests that these phenomena could be widespread in nature, and points to the existence of fundamental properties of behavioral dynamics that could drive such convergent patterns.

Animal behavior can be decomposed into a sequence of discrete activity bouts over time. Analyzing the statistical structure of such behavioral sequences can provide insights into the drivers of behavioral decisions. Laboratory studies, predominantly in invertebrates, have suggested that behavioral sequences exhibit multiple timescales and long-range memory, but whether these results can be generalized to other taxa and to animals in natural settings remains unclear. By analyzing accelerometer-inferred predictions of behavioral states in three species of social mammals (meerkats, white-nosed coatis, and spotted hyenas) in the wild, we discovered surprisingly consistent structuring of behavioral sequences across all behavioral states, all individuals, and all study species. Behavioral bouts were characterized by decreasing hazard functions, wherein the longer a behavioral bout had progressed, the less likely it was to end within the next instant. The predictability of an animal’s future behavioral state as a function of its present state always decreased as a truncated power-law for predictions made farther into the future, with very similar estimates for the power law exponent across all species. Finally, the distributions of bout durations were also heavy-tailed. Why such shared structural principles emerge remains unknown, and we explore multiple plausible explanations, including environmental non-stationarity, behavioral self-reinforcement, and the hierarchical nature of behavior. The existence of highly consistent patterns in behavioral sequences across our study species suggests that these phenomena could be widespread in nature, and points to the existence of fundamental properties of behavioral dynamics that could drive such convergent patterns. Significance statement: The study of animal behavior seeks to understand how and why animals do what they do. This pursuit of general principles governing behavior across species can be approached by first understanding when animals choose to change their behavioral states (e.g., switching from walking to standing, or to running). Using accelerometer-inferred behaviors of three social mammals, we uncover common structural ‘long timescale’ patterns in their sequences of behavior. We explore two explanations, involving either positive feedbacks or the interaction of several independent time-scales, about how such common patterns arise.

The formation of subgroups can allow group-living animals flexibility to balance the costs and benefits of sociality over time. Subgrouping dynamics emerge from individual decisions about whether and with whom to maintain cohesion, with these decisions potentially influenced by ecological, physiological, and social factors. We GPS-tracked the movements of nearly all members of three wild white-nosed coati ( Nasua narica ) social groups that differed in their demographic profiles to better understand how these highly social, frugivorous carnivores weight the relative importance of these different factors in their grouping decisions. Quantifying group movements and subgrouping patterns, we found that two of the three groups we tracked exhibited fission-fusion behaviours, with groups splitting into subgroups that persisted over varying timespans from minutes to days. In contrast, the third group remained together across the entire observation period. When groups split, they did not do so randomly; instead, individuals tended to form subgroups with the same individuals consistently over time. Assessing the drivers of subgrouping patterns revealed that subgroup membership was associated with genetic relatedness, but not physiological similarity as quantified by age and sex homophily. Our results demonstrate great variation in subgrouping patterns within a single species, while also highlighting a consistent role of relatedness in driving social preferences when subgroups form.

Animal activity patterns are highly variable and influenced by internal and external factors, including social processes. Quantifying activity patterns in natural settings can be challenging, as it is difficult to monitor animals over long time periods. Here, we developed and validated a machine-learning-based classifier to identify behavioural states from accelerometer data of wild spotted hyenas (Crocuta crocuta) , social carnivores that live in large fission–fusion societies. By combining this classifier with continuous collar-based accelerometer data from five hyenas, we generated a complete record of activity patterns over more than one month. We used these continuous behavioural sequences to investigate how past activity, individual idiosyncrasies, and social synchronization influence hyena activity patterns. We found that hyenas exhibit characteristic crepuscular-nocturnal daily activity patterns. Time spent active was independent of activity level on previous days, suggesting that hyenas do not show activity compensation. We also found limited evidence for an effect of individual identity on activity, and showed that pairs of hyenas who synchronized their activity patterns must have spent more time together. This study sheds light on the patterns and drivers of activity in spotted hyena societies, and also provides a useful tool for quantifying behavioural sequences from accelerometer data.