TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields

Critical insights into the potential risks of antipsychotic drugs to fish, including through effects on behaviour

Antipsychotic drugs (APDs) are a diverse class of neuroactive pharmaceuticals increasingly detected in surface and ground waters globally. Some APDs are classified as posing a high environmental risk, due, in part, to their tendency to bioaccumulate in wildlife, including fish. Additional risk drivers for APDs relate to their behavioural effects, potentially impacting fitness outcomes. However, standard ecotoxicological tests used in environmental risk assessment (ERA) do not currently account for these mechanisms. In this review, we critically appraise the environmental risks of APDs to fish. We begin by reading-across from human and mammalian effects data to standard ecotoxicological effects endpoints in fish. We then explore the wide range of behaviours suitable for ecotoxicological assessment of APDs (and other neuroactive) pharmaceuticals, principally through laboratory studies with zebrafish, and assess the potential for using these behavioural phenotypes to predict adverse individual- and population-level outcomes in wild fish, taking into account phenotypic plasticity. Next, we illustrate the advantages and challenges of measuring and applying behavioural endpoints for fish, including within current regulatory risk assessments. In our final analysis, the implications of relying on apical endpoints for ERA of neuroactive drugs (including APDs) are assessed and recommendations provided for the development of a more refined and tailored mechanistic approach, which would enable more robust assessment of their environmental risk(s).

Reverse engineering the control law for schooling in zebrafish using virtual reality

Revealing the evolved mechanisms that give rise to collective behavior is a central objective in the study of cellular and organismal systems. In addition, understanding the algorithmic basis of social interactions in a causal and quantitative way offers an important foundation for subsequently quantifying social deficits. Here, with virtual reality technology, we used virtual robot fish to reverse engineer the sensory-motor control of social response during schooling in a vertebrate model: juvenile zebrafish (Danio rerio). In addition to providing a highly controlled means to understand how zebrafish translate visual input into movement decisions, networking our systems allowed real fish to swim and interact together in the same virtual world. Thus, we were able to directly test models of social interactions in situ. A key feature of social response is shown to be single- and multitarget-oriented pursuit. This is based on an egocentric representation of the positional information of conspecifics and is highly robust to incomplete sensory input. We demonstrated, including with a Turing test and a scalability test for pursuit behavior, that all key features of this behavior are accounted for by individuals following a simple experimentally derived proportional derivative control law, which we termed "BioPD." Because target pursuit is key to effective control of autonomous vehicles, we evaluated-as a proof of principle-the potential use of this simple evolved control law for human-engineered systems. In doing so, we found close-to-optimal pursuit performance in autonomous vehicle (terrestrial, airborne, and watercraft) pursuit while requiring limited system-specific tuning or optimization.

Computer vision for primate behavior analysis in the wild

Advances in computer vision and increasingly widespread video-based behavioral monitoring are currently transforming how we study animal behavior. However, there is still a gap between the prospects and practical application, especially in videos from the wild. In this Perspective, we aim to present the capabilities of current methods for behavioral analysis, while at the same time highlighting unsolved computer vision problems that are relevant to the study of animal behavior. We survey state-of-the-art methods for computer vision problems relevant to the video-based study of individualized animal behavior, including object detection, multi-animal tracking, individual identification and (inter)action understanding. We then review methods for effort-efficient learning, one of the challenges from a practical perspective. In our outlook on the emerging field of computer vision for animal behavior, we argue that the field should develop approaches to unify detection, tracking, identification and (inter)action understanding in a single, video-based framework.

InteBOMB: Integrating generic object tracking and segmentation with pose estimation for animal behavior analysis

Advancements in animal behavior quantification methods have driven the development of computational ethology, enabling fully automated behavior analysis. Existing multi-animal pose estimation workflows rely on tracking-by-detection frameworks for either bottom-up or top-down approaches, requiring retraining to accommodate diverse animal appearances. This study introduces InteBOMB, an integrated workflow that enhances top-down approaches by incorporating generic object tracking, eliminating the need for prior knowledge of target animals while maintaining broad generalizability. InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings. The "background enhancement" strategy optimizes foreground-background contrastive loss, generating more discriminative correlation maps. The "online proofreading" strategy stores human-in-the-loop long-term memory and dynamic short-term memory, enabling adaptive updates to object visual features. The "automated labeling suggestion" technique reuses the visual features saved during tracking to identify representative frames for training set labeling. Additionally, the "joint behavior analysis" technique integrates these features with multimodal data, expanding the latent space for behavior classification and clustering. To evaluate the framework, six datasets of mice and six datasets of non-human primates were compiled, covering laboratory and natural scenes. Benchmarking results demonstrated a 24% improvement in zero-shot generic tracking and a 21% enhancement in joint latent space performance across datasets, highlighting the effectiveness of this approach in robust, generalizable behavior analysis.

Effects of social environment and energy efficiency on preferred swim speed in a marine generalist fish, pile perch (Phanerodon vacca)

Energy efficiency is a key component of movement strategy for many species. In fish, optimal swimming speed (Uopt) is the speed at which the mass-specific energetic cost to move a given distance is minimised. However, additional factors may influence an individual's preferred swimming speed (Upref). Activities requiring consistent sensory inputs, such as food finding, may require slower swimming speeds than Uopt. Further, although the majority of fish display some form of social behaviour, the influence of social interactions on Upref remains unclear. It is unlikely that all fish within a group will have the same Upref, and fish may therefore compromise individual Upref to swim with a conspecific. This study measured the Uopt, Upref and Upref in the presence of a conspecific (Upair) of pile perch, Phanerodon vacca, a non-migratory coastal marine generalist. Uopt was significantly higher than, and was not correlated with, Upref. Fish therefore chose to swim at speeds below their energetic optimum, possibly because slower swimming allows for greater awareness of surroundings. Mean Upair was significantly lower than the Upref of the faster fish in each pair but did not differ significantly from the Upref of the slower fish. Therefore, faster fish appear to slow their speed to remain with a slower conspecific. Our study suggests that environmental factors, including social surroundings, may be more important than energetic efficiency for determining swim speed in P. vacca. Further studies of fish species from various habitats will be necessary to elucidate the environmental and energetic factors underpinning Upref.

Reynolds rules in swarm fly behavior based on KAN transformer tracking method

The analysis of complex flight patterns and collective behaviors in swarming insects has emerged as a significant focus across biological and computational fields. Tracking these insects, like fruit fly, presents persistent challenges due to their rapid motion patterns and frequent occlusions in densely populated environments. To address these challenges, we propose a tracking method using particle filter framework combined with a Kolmogorov-Arnold Network (KAN)-Transformer model to extract the global features and fine-grained features of the trajectory. Additionally, manually annotated ground truth datasets are established to enable thorough assessment of tracking methods. Experimental results demonstrate the effectiveness and robustness of our proposed tracking method. Analysis of tracked trajectories revealed the Reynolds rules of flocking behavior.

vmTracking enables highly accurate multi-animal pose tracking in crowded environments

In multi-animal tracking, addressing occlusion and crowding is crucial for accurate behavioral analysis. However, in situations where occlusion and crowding generate complex interactions, achieving accurate pose tracking remains challenging. Therefore, we introduced virtual marker tracking (vmTracking), which uses virtual markers for individual identification. Virtual markers are labels derived from conventional markerless multi-animal tracking tools, such as multi-animal DeepLabCut (maDLC) and Social LEAP Estimates Animal Poses (SLEAP). Unlike physical markers, virtual markers exist only within the video and attribute features to individuals, enabling consistent identification throughout the entire video while keeping the animals markerless in reality. Using these markers as cues, annotations were applied to multi-animal videos, and tracking was conducted with single-animal DeepLabCut (saDLC) and SLEAP's single-animal method. vmTracking minimized manual corrections and annotation frames needed for training, efficiently tackling occlusion and crowding. Experiments tracking multiple mice, fish, and human dancers confirmed vmTracking's variability and applicability. These findings could enhance the precision and reliability of tracking methods used in the analysis of complex naturalistic and social behaviors in animals, providing a simpler yet more effective solution.

Impact of developmental temperature on neural growth, connectivity, and function

Environmental temperature dictates the developmental pace of poikilothermic animals. In Drosophila, slower development at lower temperatures results in higher brain connectivity, but the generality of such scaling across temperatures and brain regions and its impact on function are unclear. Here, we show that brain connectivity scales continuously across temperatures, in agreement with a first-principle model that postulates different metabolic constraints for the growth of the brain and the organism. The model predicts brain wiring under temperature cycles and the nonuniform temporal scaling of neural development across temperatures. Developmental temperature has notable effects on odor-driven behavior. Dissecting the circuit architecture and function of neurons in the olfactory pathway, we demonstrate that developmental temperature does not alter odor encoding in first- and second-order neurons, but it shifts the specificity of connections onto third-order neurons that mediate innate behaviors. We conclude that while some circuit computations are robust to the effects of developmental temperature on wiring, others exhibit phenotypic plasticity with possible adaptive advantages.

AnimalTA: A Step-by-Step Tutorial

Tracking animal displacement on video recording is a frequently used method in ethology, behavioral ecology, and neuroscience to characterize animal behavior in an automated and objective way. We propose here a quick-start tutorial of AnimalTA, a video tracking program, which aims to provide a free, open-source, and user-friendly tool for the scientific community. The strength of this software resides in its adaptability to various video-recording conditions, the possibility to run analysis and extract personalized data, and its simplicity of use.

Non-invasive eye tracking and retinal view reconstruction in free swimming schooling fish

Eye tracking has emerged as a key method for understanding how animals process visual information, identifying crucial elements of perception and attention. Traditional fish eye tracking often alters animal behavior due to invasive techniques, while non-invasive methods are limited to either 2D tracking or restricting animals after training. Our study introduces a non-invasive technique for tracking and reconstructing the retinal view of free-swimming fish in a large 3D arena without behavioral training. Using 3D fish bodymeshes reconstructed by DeepShapeKit, our method integrates multiple camera angles, deep learning for 3D fish posture reconstruction, perspective transformation, and eye tracking. We evaluated our approach using data from two fish swimming in a flow tank, captured from two perpendicular viewpoints, and validated its accuracy using human-labeled and synthesized ground truth data. Our analysis of eye movements and retinal view reconstruction within leader-follower schooling behavior reveals that fish exhibit negatively synchronised eye movements and focus on neighbors centered in the retinal view. These findings are consistent with previous studies on schooling fish, providing a further, indirect, validation of our method. Our approach offers new insights into animal attention in naturalistic settings and potentially has broader implications for studying collective behavior and advancing swarm robotics.

SAUSI: an integrative assay for measuring social aversion and motivation

Social aversion is a key feature of numerous mental health disorders such as Social Anxiety and Autism Spectrum Disorders. Nevertheless, the biobehavioral mechanisms underlying social aversion remain poorly understood. Progress in understanding the etiology of social aversion has been hindered by the lack of comprehensive tools to assess social aversion in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which integrates elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social aversion in mice. Using this novel assay, we found that social isolation-induced social aversion in mice is largely driven by increases in social fear and social motivation. Deep learning analyses revealed a unique behavioral footprint underlying the socially aversive state produced by isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Social aversion was further assessed using traditional assays - including the 3-chamber sociability assay and the resident intruder assay - which were sufficient to reveal fragments of a social aversion phenotype, including changes to either social motivation or social interaction, but which failed to provide a wholistic assessment of social aversion. Critically, these assays were not sufficient to reveal key components of social aversion, including social freezing and social hesitancy behaviors. Lastly, we demonstrated that SAUSI is generalizable, as it can be used to assess social aversion induced by non-social stressors, such as foot shock. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social aversion in rodents.

Integrating computer vision and molecular neurobiology to bridge the gap between behavior and the brain

The past decade of social insect research has seen rapid development in automated behavioral tracking and molecular profiling of the nervous system, two distinct but complementary lines of inquiry into phenotypic variation across individuals, colonies, populations, and species. These experimental strategies have developed largely in parallel, as automated tracking generates a continuous stream of behavioral data, while, in contrast, 'omics-based profiling provides a single 'snapshot' of the brain. Better integration of these approaches applied to studying variation in social behavior will reveal the underlying genetic and neurobiological mechanisms that shape the evolution and diversification of social life. In this review, we discuss relevant advances in both fields and propose new strategies to better elucidate the molecular and behavioral innovations that generate social life.

Fine-scale movement response of juvenile brown trout to hydropeaking

Juvenile fish are known to be the most impacted during hydropeaking events due to stranding or uncontrolled drift resulting from changes to water depth and flow velocity. To shed light on their response to such hydraulic alterations, we coupled flume experiments with image-based fish tracking and quantified the fine-scale movement behavior of wild (n = 30) and hatchery-reared (n = 38) brown trout (Salmo trutta) parr. We exposed fish to two distinct hydropeaking treatments in a laterally inclined (14 %) flume section stocked with real cobbles to create refuge and heterogeneous hydraulic conditions. Fish were individually acclimated (20 min) to baseflow (Q = 1.6 L s-1) and then exposed to three consecutive hydropeaking events, reaching peakflows tenfold larger than baseflow (Q = 16 L s-1). We found that, within just minutes, fish exhibited fine-scale movement responses to cope with the change of hydrodynamic conditions. Fish moved perpendicular to the main flow direction to shallow areas as these became submerged during discharge increase, holding position at low velocity zones. This resulted in a significant difference (p < 0.001) in lateral occupancy of the experimental section between baseflow and peakflow. During peakflow, fish occupied specific positions around cobbles and exhibited swimming behaviors, including bow-riding and entraining, that allowed them to hold position while likely minimizing energy expenditure. As a result, swimming distance reduced 60-70 % compared to baseflow. During the decrease in discharge following peakflow, fish abandoned areas falling dry by moving laterally. In the treatment with the larger down-ramping rate, the time to initiate relocation was lower while the relocation speed was higher. This study shows that, for the conditions investigated here, brown trout parr is capable of swiftly deploying multiple behavioral responses to navigate rapid changes in hydrodynamic conditions. These findings can be incorporated into habitat modeling and improve our capacity to inform hydropeaking mitigation efforts.

Segmentation tracking and clustering system enables accurate multi-animal tracking of social behaviors

Accurate analysis of social behaviors in animals is hindered by methodological challenges. Here, we develop a segmentation tracking and clustering system (STCS) to address two major challenges in computational neuroethology: reliable multi-animal tracking and pose estimation under complex interaction conditions and providing interpretable insights into social differences guided by genotype information. We established a comprehensive, long-term, multi-animal-tracking dataset across various experimental settings. Benchmarking STCS against state-of-the-art tracking algorithms, we demonstrated its superior efficacy in analyzing behavioral experiments and establishing a robust tracking baseline. By analyzing the behavior of mice with autism spectrum disorder (ASD) using a novel weakly supervised clustering method under both solitary and social conditions, STCS reveals potential links between social stress and motor impairments. Benefiting from its modular and web-based design, STCS allows researchers to easily integrate the latest computer vision methods, enabling comprehensive behavior analysis services over the Internet, even from a single laptop.

Immune challenge affects risk sensitivity and locomotion in mosquitofish (Gambusia holbrooki)

The immune system is crucial in responding to disease-causing pathogens. However, immune responses may also cause stereotypical changes in behaviour known as sickness behaviours, which often include reduced activity. Sickness behaviours are thought to have an important role in conserving energy required to support the immune response; however, little is known about how they manifest over time or in relation to risk, particularly in fishes. Here, we induced an immune response in mosquitofish (Gambusia holbrooki) by inoculating them with Escherichia coli lipopolysaccharide (LPS). We subsequently tested batches of fish at 24 h intervals and examined: locomotory behaviour, tendency to use a refuge and fast-start response immediately following a threat stimulus (measured as peak acceleration). Control and LPS-treated fish behaved similarly on days 1, 3 and 4. However, 2 days post-inoculation, LPS fish swam more slowly and spent more time in the refuge than control fish, although no difference in post-threat peak acceleration was found. Our findings suggest that sickness behaviours peak roughly 2 days following exposure to LPS and are relatively short-lived. Specifically, immune-challenged individuals exhibit reduced locomotion and exploratory behaviour, becoming more risk averse overall while still retaining the ability to respond acutely to a threat stimulus.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

GoFish: a foray into open-source, aquatic behavioral automation

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.

Lessons in cognition: A review of maze designs and procedures used to measure spatial learning in fish

The use of different mazes to assess spatial learning has become more common in fish behavior studies in recent decades. This increase in fish cognition research has opened the door to numerous possibilities for exciting and diverse questions, such as identifying ecological drivers of spatial cognition and understanding the role individual variation plays in navigational abilities. There are many different types of mazes, each with its own specific considerations, making it challenging to determine exactly which spatial test is the most relevant and appropriate for a particular experiment. Many spatial mazes, such as the T-maze and Y-maze, have been successfully adapted from rodent studies, particularly with respect to zebrafish, a widely accepted non-mammalian model in biomedical studies. Standardization across studies is increasing with these easily accessible maze designs, validating them for use in fish; however, variations in design (e.g., length of arms and scale) and procedure still exist, and the impact of these variations on results is largely unknown. The efforts to standardize mazes outside zebrafish work are also more limited. Other mazes have been developed specifically for use on fish, with design modifications varying widely, making it difficult to draw comparisons. In this review, we have highlighted the many design and procedural elements that should be considered for the acquisition of reliable behavioral data, with the goal of drawing readers' attention to aspects of experimentation that are often not given the careful consideration that they deserve. We then argue that additional focused research and reporting is needed to produce more reliable methods in spatial learning research across a broader range of subjects.

Probing Asymmetric Interactions with Time-Separated Mutual Information: A Case Study Using Golden Shiners

Leader-follower modalities and other asymmetric interactions that drive the collective motion of organisms are often quantified using information theory metrics like transfer or causation entropy. These metrics are difficult to accurately evaluate without a much larger number of data than is typically available from a time series of animal trajectories collected in the field or from experiments. In this paper, we use a generalized leader-follower model to argue that the time-separated mutual information between two organism positions can serve as an alternative metric for capturing asymmetric correlations that is much less data intensive and more accurately estimated by popular k-nearest neighbor algorithms than transfer entropy. Our model predicts a local maximum of this mutual information at a time separation value corresponding to the fundamental reaction timescale of the follower organism. We confirm this prediction by analyzing time series trajectories recorded for a pair of golden shiner fish circling an annular tank.

Fish couple forecasting with feedback control to chase and capture moving prey

Predator-prey interactions are fundamental to ecological and evolutionary dynamics. Yet, predicting the outcome of such interactions-whether predators intercept prey or fail to do so-remains a challenge. An emerging hypothesis holds that interception trajectories of diverse predator species can be described by simple feedback control laws that map sensory inputs to motor outputs. This form of feedback control is widely used in engineered systems but suffers from degraded performance in the presence of processing delays such as those found in biological brains. We tested whether delay-uncompensated feedback control could explain predator pursuit manoeuvres using a novel experimental system to present hunting fish with virtual targets that manoeuvred in ways that push the limits of this type of control. We found that predator behaviour cannot be explained by delay-uncompensated feedback control, but is instead consistent with a pursuit algorithm that combines short-term forecasting of self-motion and prey motion with feedback control. This model predicts both predator interception trajectories and whether predators capture or fail to capture prey on a trial-by-trial basis. Our results demonstrate how animals can combine short-term forecasting with feedback control to generate robust flexible behaviours in the face of significant processing delays.

Fish communicate with water flow to enhance a school's social network

Schooling fish rely on a social network created through signaling between its members to interact with their environment. Previous studies have established that vision is necessary for schooling and that flow sensing by the lateral line system may aid in a school's cohesion. However, it remains unclear to what extent flow provides a channel of communication between schooling fish. Based on kinematic measurements of the speed and heading of schooling tetras (Petitella rhodostoma), we found that compromising the lateral line by chemical treatment reduced the mutual information between individuals by ∼13%. This relatively small reduction in pairwise communication propagated through schools of varying size to reduce the degree and connectivity of the social network by more than half. Treated schools additionally showed more than twice the spatial heterogeneity of fish with unaltered flow sensing. These effects were much more substantial than the changes that we measured in the nearest-neighbor distance, speed and intermittency of individual fish by compromising flow sensing. Therefore, flow serves as a valuable supplement to visual communication in a manner that is revealed through a school's network properties.

Behavioral adjustment of C. elegans to mechanosensory loss requires intact mechanosensory neurons

Sensory neurons specialize in detecting and signaling the presence of diverse environmental stimuli. Neuronal injury or disease may undermine such signaling, diminishing the availability of crucial information. Can animals distinguish between a stimulus not being present and the inability to sense that stimulus in the first place? To address this question, we studied Caenorhabditis elegans nematode worms that lack gentle body touch sensation due to genetic mechanoreceptor dysfunction. We previously showed that worms can compensate for the loss of touch by enhancing their sense of smell, via an FLP-20 neuropeptide pathway. Here, we find that touch-deficient worms exhibit, in addition to sensory compensation, also cautious-like behavior, as if preemptively avoiding potential undetectable hazards. Intriguingly, these behavioral adjustments are abolished when the touch neurons are removed, suggesting that touch neurons are required for signaling the unavailability of touch information, in addition to their conventional role of signaling touch stimulation. Furthermore, we found that the ASE taste neurons, which similarly to the touch neurons, express the FLP-20 neuropeptide, exhibit altered FLP-20 expression levels in a touch-dependent manner, thus cooperating with the touch circuit. These results imply a novel form of neuronal signaling that enables C. elegans to distinguish between lack of touch stimulation and loss of touch sensation, producing adaptive behavioral adjustments that could overcome the inability to detect potential threats.

Circadian regulation of locomotion, respiration, and arousability in adult blacklegged ticks (Ixodes scapularis)

The blacklegged tick, Ixodes scapularis, is an ectoparasitic arachnid and vector for infectious diseases, including Lyme borreliosis. Here, we investigate the diurnal activity and respiration of wild-caught and lab-reared adult ticks with long-term video recording, multi-animal tracking and high-resolution respirometry. We find male and female ticks are in a more active, more arousable state during circadian night. We find respiration is augmented by light, with dark onset triggering more frequent bouts of discontinuous gas exchange and a higher overall volume of CO2 respired. Observed inactivity during the day meets the criteria of sleep: homeostatic in nature, rapidly reversible, a characteristic pose, and reduced arousal threshold. Our findings indicate that blacklegged ticks are in a distinct, heightened state of activity and arousability during night and in dark, suggesting this period may carry higher risk for tick bites and subsequent contraction of tick-borne diseases.

Diving deep: zebrafish models in motor neuron degeneration research

In the dynamic landscape of biomedical science, the pursuit of effective treatments for motor neuron disorders like hereditary spastic paraplegia (HSP), amyotrophic lateral sclerosis (ALS), and spinal muscular atrophy (SMA) remains a key priority. Central to this endeavor is the development of robust animal models, with the zebrafish emerging as a prime candidate. Exhibiting embryonic transparency, a swift life cycle, and significant genetic and neuroanatomical congruencies with humans, zebrafish offer substantial potential for research. Despite the difference in locomotion-zebrafish undulate while humans use limbs, the zebrafish presents relevant phenotypic parallels to human motor control disorders, providing valuable insights into neurodegenerative diseases. This review explores the zebrafish's inherent traits and how they facilitate profound insights into the complex behavioral and cellular phenotypes associated with these disorders. Furthermore, we examine recent advancements in high-throughput drug screening using the zebrafish model, a promising avenue for identifying therapeutically potent compounds.

MCP: Multi-Chicken Pose Estimation Based on Transfer Learning

Poultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the "multi-chicken pose" (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis.

Behavioral transition of a fish school in a crowded environment

In open water, social fish gather to form schools, in which fish generally align with each other. In this work, we study how this social behavior evolves when perturbed by artificial obstacles. We measure the behavior of a group of zebrafish in the presence of a periodic array of pillars. When the pillar density is low, the fish regroup with a typical interdistance and a well-polarized state with parallel orientations, similarly to their behavior in open-water conditions. Above a critical density of pillars, their social interactions, which are mostly based on vision, are screened and the fish spread randomly through the aquarium, orienting themselves along the free axes of the pillar lattice. The abrupt transition from natural to artificial orientation happens when the pillar interdistance is comparable to the social distance of the fish, i.e., their most probable interdistance. We develop a stochastic model of the relative orientation between fish pairs, taking into account alignment, antialignment, and tumbling, from a distribution biased by the environment. This model provides a good description of the experimental probability distribution of the relative orientation between the fish and captures the behavioral transition. Using the model to fit the experimental data provides qualitative information on the evolution of cognitive parameters, such as the alignment or the tumbling rates, as the pillar density increases. At high pillar density, we find that the artificial environment imposes its geometrical constraints to the fish school, drastically increasing the tumbling rate.

Antipredator defences in motion: animals reduce predation risks by concealing or misleading motion signals

Motion is a crucial part of the natural world, yet our understanding of how animals avoid predation whilst moving remains rather limited. Although several theories have been proposed for how antipredator defence may be facilitated during motion, there is often a lack of supporting empirical evidence, or conflicting findings. Furthermore, many studies have shown that motion often 'breaks' camouflage, as sudden movement can be detected even before an individual is recognised. Whilst some static camouflage strategies may conceal moving animals to a certain extent, more emphasis should be given to other modes of camouflage and related defences in the context of motion (e.g. flicker fusion camouflage, active motion camouflage, motion dazzle, and protean motion). Furthermore, when motion is involved, defence strategies are not necessarily limited to concealment. An animal can also rely on motion to mislead predators with regards to its trajectory, location, size, colour pattern, or even identity. In this review, we discuss the various underlying antipredator strategies and the mechanisms through which they may be linked to motion, conceptualising existing empirical and theoretical studies from two perspectives - concealing and misleading effects. We also highlight gaps in our understanding of these antipredator strategies, and suggest possible methodologies for experimental designs/test subjects (i.e. prey and/or predators) and future research directions.

Spatial confinement affects the heterogeneity and interactions between shoaling fish

Living objects are able to consume chemical energy and process information independently from others. However, living objects can coordinate to form ordered groups such as schools of fish. This work considers these complex groups as living materials and presents imaging-based experiments of laboratory schools of fish to understand how activity, which is a non-equilibrium feature, affects the structure and dynamics of a group. We use spatial confinement to control the motion and structure of fish within quasi-2D shoals of fish and use image analysis techniques to make quantitative observations of the structures, their spatial heterogeneity, and their temporal fluctuations. Furthermore, we utilize Monte Carlo simulations to replicate the experimentally observed data which provides insight into the effective interactions between fish and confirms the presence of a confinement-based behavioral preference transition. In addition, unlike in short-range interacting systems, here structural heterogeneity and dynamic activities are positively correlated as a result of complex interplay between spatial arrangement and behavioral dynamics in fish collectives.

Revealing the mechanism and function underlying pairwise temporal coupling in collective motion

Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.

Collective search in ants: Movement determines footprints, and footprints influence movement

Collectively searching animals might be expected to coordinate with their groupmates to cover ground more evenly or efficiently than uncoordinated groups. Communication can lead to coordination in many ways. Previous work in ants suggests that chemical 'footprints', left behind by individuals as they walk, might serve this function by modulating the movement patterns of following ants. Here, we test this hypothesis by considering the two predictions that, first, ants may turn away from sites with higher footprint concentrations (klinotaxis), or, second, that they may change their turning patterns depending on the presence of footprints (klinokinesis). We tracked 5 whole colonies of Temnothorax rugatulus ants in a large arena over 5h. We approximated the footprint concentration by summing ant visitations for each point in the arena and calculated the speed and local path straightness for each point of the ant trajectories. We counterintuitively find that ants walk slightly faster and straighter in areas with fewer footprints. This is partially explained by the effect that ants who start out from the nest walking straighter move on average further away from the nest, where there are naturally fewer footprints, leading to an apparent relationship between footprint density and straightness However, ants walk slightly faster and straighter off footprints even when controlling for this effect. We tested for klinotaxis by calculating the footprint concentrations perceived by the left and right antennae of ants and found no evidence for a turning-away (nor turning-towards) behavior. Instead, we found noticeable effects of environmental idiosyncrasies on the behavior of ants which are likely to overpower any reactions to pheromones. Our results indicate that search density around an ant colony is affected by several independent processes, including individual differences in movement pattern, local spatial heterogeneities, and ants' reactions to chemical footprints. The multitude of effects illustrates that non-communicative coordination, individual biases and interactions with the environment might have a greater impact on group search efficiency and exploratory movements than pheromone communication.

Substrate evaporation drives collective construction in termites

Termites build complex nests which are an impressive example of self-organization. We know that the coordinated actions involved in the construction of these nests by multiple individuals are primarily mediated by signals and cues embedded in the structure of the nest itself. However, to date there is still no scientific consensus about the nature of the stimuli that guide termite construction, and how they are sensed by termites. In order to address these questions, we studied the early building behavior of Coptotermes gestroi termites in artificial arenas, decorated with topographic cues to stimulate construction. Pellet collections were evenly distributed across the experimental setup, compatible with a collection mechanism that is not affected by local topography, but only by the distribution of termite occupancy (termites pick pellets at the positions where they are). Conversely, pellet depositions were concentrated at locations of high surface curvature and at the boundaries between different types of substrate. The single feature shared by all pellet deposition regions was that they correspond to local maxima in the evaporation flux. We can show analytically and we confirm experimentally that evaporation flux is directly proportional to the local curvature of nest surfaces. Taken together, our results indicate that surface curvature is sufficient to organize termite building activity and that termites likely sense curvature indirectly through substrate evaporation. Our findings reconcile the apparently discordant results of previous studies.

VespAI: a deep learning-based system for the detection of invasive hornets

The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to pollinators in Europe and East Asia. To effectively limit its spread, colonies must be detected and destroyed early in the invasion curve, however the current reliance upon visual alerts by the public yields low accuracy. Advances in deep learning offer a potential solution to this, but the application of such technology remains challenging. Here we present VespAI, an automated system for the rapid detection of V. velutina. We leverage a hardware-assisted AI approach, combining a standardised monitoring station with deep YOLOv5s architecture and a ResNet backbone, trained on a bespoke end-to-end pipeline. This enables the system to detect hornets in real-time-achieving a mean precision-recall score of ≥0.99-and send associated image alerts via a compact remote processor. We demonstrate the successful operation of a prototype system in the field, and confirm its suitability for large-scale deployment in future use cases. As such, VespAI has the potential to transform the way that invasive hornets are managed, providing a robust early warning system to prevent ingressions into new regions.

Predicting the long-term collective behaviour of fish pairs with deep learning

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.

Real-Time Assessment of Rodent Engagement Using ArUco Markers: A Scalable and Accessible Approach for Scoring Behavior in a Nose-Poking Go/No-Go Task

In the field of behavioral neuroscience, the classification and scoring of animal behavior play pivotal roles in the quantification and interpretation of complex behaviors displayed by animals. Traditional methods have relied on video examination by investigators, which is labor-intensive and susceptible to bias. To address these challenges, research efforts have focused on computational methods and image-processing algorithms for automated behavioral classification. Two primary approaches have emerged: marker- and markerless-based tracking systems. In this study, we showcase the utility of "Augmented Reality University of Cordoba" (ArUco) markers as a marker-based tracking approach for assessing rat engagement during a nose-poking go/no-go behavioral task. In addition, we introduce a two-state engagement model based on ArUco marker tracking data that can be analyzed with a rectangular kernel convolution to identify critical transition points between states of engagement and distraction. In this study, we hypothesized that ArUco markers could be utilized to accurately estimate animal engagement in a nose-poking go/no-go behavioral task, enabling the computation of optimal task durations for behavioral testing. Here, we present the performance of our ArUco tracking program, demonstrating a classification accuracy of 98% that was validated against the manual curation of video data. Furthermore, our convolution analysis revealed that, on average, our animals became disengaged with the behavioral task at ∼75 min, providing a quantitative basis for limiting experimental session durations. Overall, our approach offers a scalable, efficient, and accessible solution for automated scoring of rodent engagement during behavioral data collection.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

An effective hydrodynamic description of marching locusts

A fundamental question in complex systems is how to relate interactions between individual components ('microscopic description') to the global properties of the system ('macroscopic description'). Furthermore, it is unclear whether such a macroscopic description exists and if such a description can capture large-scale properties. Here, we address the validity of a macroscopic description of a complex biological system using the collective motion of desert locusts as a canonical example. One of the world's most devastating insect plagues begins when flightless juvenile locusts form 'marching bands'. These bands display remarkable coordinated motion, moving through semiarid habitats in search of food. We investigated how well macroscopic physical models can describe the flow of locusts within a band. For this, we filmed locusts within marching bands during an outbreak in Kenya and automatically tracked all individuals passing through the camera frame. We first analyzed the spatial topology of nearest neighbors and found individuals to be isotropically distributed. Despite this apparent randomness, a local order was observed in regions of high density in the radial distribution function, akin to an ordered fluid. Furthermore, reconstructing individual locust trajectories revealed a highly aligned movement, consistent with the one-dimensional version of the Toner-Tu equations, a generalization of the Navier-Stokes equations for fluids, used to describe the equivalent macroscopic fluid properties of active particles. Using this effective Toner-Tu equation, which relates the gradient of the pressure to the acceleration, we show that the effective 'pressure' of locusts increases as a linear function of density in segments with the highest polarization (for which the one-dimensional approximation is most appropriate). Our study thus demonstrates an effective hydrodynamic description of flow dynamics in plague locust swarms.

The human neuropsychiatric risk gene Drd2 is necessary for social functioning across evolutionary distant species

The Drd2 gene, encoding the dopamine D2 receptor (D2R), was recently indicated as a potential target in the etiology of lowered sociability (i.e., social withdrawal), a symptom of several neuropsychiatric disorders such as Schizophrenia and Major Depression. Many animal species show social withdrawal in response to stimuli, including the vinegar fly Drosophila melanogaster and mice, which also share most human disease-related genes. Here we will test for causality between Drd2 and sociability and for its evolutionary conserved function in these two distant species, as well as assess its mechanism as a potential therapeutic target. During behavioral observations in groups of freely interacting D. melanogaster, Drd2 homologue mutant showed decreased social interactions and locomotor activity. After confirming Drd2's social effects in flies, conditional transgenic mice lacking Drd2 in dopaminergic cells (autoreceptor KO) or in serotonergic cells (heteroreceptor KO) were studied in semi-natural environments, where they could freely interact. Autoreceptor KOs showed increased sociability, but reduced activity, while no overall effect of Drd2 deletion was observed in heteroreceptor KOs. To determine acute effects of D2R signaling on sociability, we also showed that a direct intervention with the D2R agonist Sumanirole decreased sociability in wild type mice, while the antagonist showed no effects. Using a computational ethological approach, this study demonstrates that Drd2 regulates sociability across evolutionary distant species, and that activation of the mammalian D2R autoreceptor, in particular, is necessary for social functioning.

Visually guided and context-dependent spatial navigation in the translucent fish Danionella cerebrum

Danionella cerebrum (DC) is a promising vertebrate animal model for systems neuroscience due to its small adult brain volume and inherent optical transparency, but the scope of their cognitive abilities remains an area of active research. In this work, we established a behavioral paradigm to study visual spatial navigation in DC and investigate their navigational capabilities and strategies. We initially observed that adult DC exhibit strong negative phototaxis in groups but less so as individuals. Using their dark preference as a motivator, we designed a spatial navigation task inspired by the Morris water maze. Through a series of environmental cue manipulations, we found that DC utilize visual cues to anticipate a reward location and found evidence for landmark-based navigational strategies wherein DC could use both proximal and distal visual cues. When subsets of proximal visual cues were occluded, DC were capable of using distant contextual visual information to solve the task, providing evidence for allocentric spatial navigation. Without proximal visual cues, DC tended to seek out a direct line of sight with at least one distal visual cue while maintaining a positional bias toward the reward location. In total, our behavioral results suggest that DC can be used to study the neural mechanisms underlying spatial navigation with cellular resolution imaging across an adult vertebrate brain.

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.

Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology

Aquatic eco-neurotoxicology is an emerging field that requires new analytical systems to study the effects of pollutants on animal behaviors. This is especially true if we are to gain insights into one of the least studied aspects: the potential perturbations that neurotoxicants can have on cognitive behaviors. The paucity of experimental data is partly caused by a lack of low-cost technologies for the analysis of higher-level neurological functions (e.g., associative learning) in small aquatic organisms. Here, we present a proof-of-concept prototype that utilizes a new real-time animal tracking software for on-the-fly video analysis and closed-loop, external hardware communications to deliver stimuli based on specific behaviors in aquatic organisms, spanning three animal phyla: chordates (fish, frog), platyhelminthes (flatworm), and arthropods (crustacean). The system's open-source software features an intuitive graphical user interface and advanced adaptive threshold-based image segmentation for precise animal detection. We demonstrate the precision of animal tracking across multiple aquatic species with varying modes of locomotion. The presented technology interfaces easily with low-cost and open-source hardware such as the Arduino microcontroller family for closed-loop stimuli control. The new system has potential future applications in eco-neurotoxicology, where it could enable new opportunities for cognitive research in diverse small aquatic model organisms.

replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Tuning social interactions' strength drives collective response to light intensity in schooling fish

Schooling fish heavily rely on visual cues to interact with neighbors and avoid obstacles. The availability of sensory information is influenced by environmental conditions and changes in the physical environment that can alter the sensory environment of the fish, which in turn affects individual and group movements. In this study, we combine experiments and data-driven modeling to investigate the impact of varying levels of light intensity on social interactions and collective behavior in rummy-nose tetra fish. The trajectories of single fish and groups of fish swimming in a tank under different lighting conditions were analyzed to quantify their movements and spatial distribution. Interaction functions between two individuals and the fish interaction with the tank wall were reconstructed and modeled for each light condition. Our results demonstrate that light intensity strongly modulates social interactions between fish and their reactions to obstacles, which then impact collective motion patterns that emerge at the group level.

Energetics of collective movement in vertebrates

The collective directional movement of animals occurs over both short distances and longer migrations, and is a critical aspect of feeding, reproduction and the ecology of many species. Despite the implications of collective motion for lifetime fitness, we know remarkably little about its energetics. It is commonly thought that collective animal motion saves energy: moving alone against fluid flow is expected to be more energetically expensive than moving in a group. Energetic conservation resulting from collective movement is most often inferred from kinematic metrics or from computational models. However, the direct measurement of total metabolic energy savings during collective motion compared with solitary movement over a range of speeds has yet to be documented. In particular, longer duration and higher speed collective motion must involve both aerobic and non-aerobic (high-energy phosphate stores and substrate-level phosphorylation) metabolic energy contributions, and yet no study to date has quantified both types of metabolic contribution in comparison to locomotion by solitary individuals. There are multiple challenging questions regarding the energetics of collective motion in aquatic, aerial and terrestrial environments that remain to be answered. We focus on aquatic locomotion as a model system to demonstrate that understanding the energetics and total cost of collective movement requires the integration of biomechanics, fluid dynamics and bioenergetics to unveil the hydrodynamic and physiological phenomena involved and their underlying mechanisms.

NAPS: Integrating pose estimation and tag-based tracking

1. Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. 2. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state-of-the-art, deep learning-based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). 3. We provide a stand-alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. 4. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.

OptoPi: An open source flexible platform for the analysis of small animal behaviour

Behaviour is the ultimate output of neural circuit computations, and therefore its analysis is a cornerstone of neuroscience research. However, every animal and experimental paradigm requires different illumination conditions to capture and, in some cases, manipulate specific behavioural features. This means that researchers often develop, from scratch, their own solutions and experimental set-ups. Here, we present OptoPi, an open source, affordable (∼ £600), behavioural arena with accompanying multi-animal tracking software. The system features highly customisable and reproducible visible and infrared illumination and allows for optogenetic stimulation. OptoPi acquires images using a Raspberry Pi camera, features motorised LED-based illumination, Arduino control, as well as irradiance monitoring to fine-tune illumination conditions with real time feedback. Our open-source software (BioImageProcessing) can be used to simultaneously track multiple unmarked animals both in on-line and off-line modes. We demonstrate the functionality of OptoPi by recording and tracking under different illumination conditions the spontaneous behaviour of larval zebrafish as well as adult Drosophila flies and their first instar larvae, an experimental animal that due to its small size and transparency has classically been hard to track. Further, we showcase OptoPi's optogenetic capabilities through a series of experiments using transgenic Drosophila larvae.

SMART-BARN: Scalable multimodal arena for real-time tracking behavior of animals in large numbers

The SMART-BARN (scalable multimodal arena for real-time tracking behavior of animals in large numbers) achieves fast, robust acquisition of movement, behavior, communication, and interactions of animals in groups, within a large (14.7 meters by 6.6 meters by 3.8 meters), three-dimensional environment using multiple information channels. Behavior is measured from a wide range of taxa (insects, birds, mammals, etc.) and body size (from moths to humans) simultaneously. This system integrates multiple, concurrent measurement techniques including submillimeter precision and high-speed (300 hertz) motion capture, acoustic recording and localization, automated behavioral recognition (computer vision), and remote computer-controlled interactive units (e.g., automated feeders and animal-borne devices). The data streams are available in real time allowing highly controlled and behavior-dependent closed-loop experiments, while producing comprehensive datasets for offline analysis. The diverse capabilities of SMART-BARN are demonstrated through three challenging avian case studies, while highlighting its broad applicability to the fine-scale analysis of collective animal behavior across species.

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

Immune challenge affects reproductive behaviour in the guppy (Poecilia reticulata)

Immunocompetence and reproduction are among the most important determinants of fitness. However, energetic and metabolic constraints create conflict between these two life-history traits. While many studies have explored the relationship between immune activity and reproductive fitness in birds and mammals inoculated with bacterial endotoxin, very few have focused on fish. Fish have been neglected in this area due, in part, to the claim that they are largely resistant to the immune effects of endotoxins. However, the present study suggests that they are susceptible to significant effects with respect to reproductive behaviour. Here, we examined the reproductive behaviour of male guppies following exposure to bacterial lipopolysaccharides (LPS) in comparison to that of male guppies in a control treatment. Additionally, we investigated the responses of females to these males. We show that although immune challenge does not suppress general activity in male guppies, it significantly reduces mating effort. While females showed no difference in general activity as a function of male treatments, they did exhibit reduced group cohesion in the presence of LPS-exposed males. We discuss this in the context of sickness behaviours, social avoidance of immune-challenged individuals and the effects of mounting an immune response on reproductive behaviour.

Guppies in large groups cooperate more frequently in an experimental test of the group size paradox

The volunteer's dilemma, in which a single individual is required to produce a public good, predicts that individuals in larger groups will cooperate less frequently. Mechanistically, this could result from trade-offs between costs associated with volunteering and costs incurred if the public good is not produced (nobody volunteers). During predator inspection, one major contributor to the cost of volunteering is likely increased probability of predation; however, a predator also poses a risk to all individuals if nobody inspects. We tested the prediction that guppies in larger groups will inspect a predator less than those in smaller groups. We also predicted that individuals in larger groups would perceive less threat from the predator stimulus because of the protective benefits of larger groups (e.g. dilution). Contrary to prediction, we found that individuals in large groups inspected more frequently than those in smaller groups, but (as predicted) spent less time in refuges. There was evidence that individuals in intermediate-sized groups made fewest inspections and spent most time in refuges, suggesting that any link between group size, risk and cooperation is not driven by simple dilution. Extensions of theoretical models that capture these dynamics will likely be broadly applicable to risky cooperative behaviour.