Challenges and solutions for studying collective animal behaviour in the wild

Individual and ecological heterogeneity promote complex communication in social vertebrate group decisions

To receive the benefits of social living, individuals must make effective group decisions that enable them to achieve behavioural coordination and maintain cohesion. However, heterogeneity in the physical and social environments surrounding group decision-making contexts can increase the level of difficulty social organisms face in making decisions. Groups that live in variable physical environments (high ecological heterogeneity) can experience barriers to information transfer and increased levels of ecological uncertainty. In addition, in groups with large phenotypic variation (high individual heterogeneity), individuals can have substantial conflicts of interest regarding the timing and nature of activities, making it difficult for them to coordinate their behaviours or reach a consensus. In such cases, active communication can increase individuals' abilities to achieve coordination, such as by facilitating the transfer and aggregation of information about the environment or individual behavioural preferences. Here, we review the role of communication in vertebrate group decision-making and its relationship to heterogeneity in the ecological and social environment surrounding group decision-making contexts. We propose that complex communication has evolved to facilitate decision-making in specific socio-ecological contexts, and we provide a framework for studying this topic and testing related hypotheses as part of future research in this area. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Collective decision-making and spatial patterns in orientation of an endemic ungulate on the Tibetan Plateau

Group living animals form striking aggregation patterns and display synchronization, polarization, and collective intelligence. Though many collective behavioral studies have been conducted on small animals like insects and fish, research on large animals is still rare due to the limited availability of field collective data. We used drones to record videos and analyzed the decision-making and behavioral spatial patterns in orientation of Kiang (Tibetan wild ass, Equus kiang). Leadership is unevenly distributed among Kiang, with the minority initiating majority behavior-shift decisions. Decisions of individual to join are driven by imitation between group members, and are largely dependent on the number of members who have already joined. Kiang respond to the behavior and position of neighbors through different strategies. They strongly polarize when moving, therefore adopting a linear alignment. When vigilant, orientation deviation increases as they form a tighter group. They remain scattered while feeding and, in that context, adopt a side-by-side alignment. This study reveals partially-shared decision-making among Kiang, whereby copying neighbors provides the wisdom to thrive in harsh conditions. This study also suggests that animals' spatial patterns in orientation depend largely on their behavioral states in achieving synchronization.

Lag and duration of leader-follower relationships in mixed traffic using causal inference

This study presents comprehensive analysis of car-following behavior on roads, utilizing Granger causality and transfer entropy techniques to enhance the validity of existing car-following models. It was found that most leader-follower relationships exhibit a delay in lateral movement by 4-5 s and last for short periods of around 3-5 s. These patterns are exhibited for all types of relationship found in the dataset, as well as for followers of all types. These findings imply that lateral movement reactions are governed by a different set of rules from braking and acceleration reactions, and the advantage in following lateral changes is short-lived. This also suggests that mixed traffic conditions may force drivers to slow down and calibrate reactions, as well as limiting the speed advantage gained by following a leader. Our methods were verified against random sampling as a method of selecting leader-follower pairs, decreasing the percent error in predicted speeds by 9.5% using the optimal velocity car-following model. The study concludes with a set of recommendations for future work, including the use of a diversity of car-following models for simulation and the use of causation entropy to distinguish between direct and indirect influences.

The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

Mechanisms of group-hunting in vertebrates

Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.

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.

Schooling of light reflecting fish

One of the hallmarks of the collective movement of large schools of pelagic fish are waves of shimmering flashes that propagate across the school, usually following an attack by a predator. Such flashes arise when sunlight is reflected off the specular (mirror-like) skin that characterizes many pelagic fishes, where it is otherwise thought to offer a means for camouflage in open waters. While it has been suggested that these 'shimmering waves' are a visual manifestation of the synchronized escape response of the fish, the phenomenon has been regarded only as an artifact of esthetic curiosity. In this study we apply agent-based simulations and deep learning techniques to show that, in fact, shimmering waves contain information on the behavioral dynamics of the school. Our analyses are based on a model that combines basic rules of collective motion and the propagation of light beams in the ocean, as they hit and reflect off the moving fish. We use the resulting reflection patterns to infer the essential dynamics and inter-individual interactions which are necessary to generate shimmering waves. Moreover, we show that light flashes observed by the school members themselves may extend the range at which information can be communicated across the school. Assuming that fish pay heed to this information, for example by entering an apprehensive state of reduced response-time, our analysis suggests that it can speed up the propagation of information across the school. Further still, we use an artificial neural network to show that light flashes are, on their own, indicative of the state and dynamics of the school, and are sufficient to infer the direction of attack and the shape of the school with high accuracy.

Drone Observation for the Quantitative Study of Complex Multilevel Societies

Unmanned aerial vehicles (drones) have recently been used in various behavioral ecology studies. However, their application has been limited to single groups, and most studies have not implemented individual identification. A multilevel society refers to a social structure in which small stable "core units" gather and make a larger, multiple-unit group. Here, we introduce recent applications of drone technology and individual identification to complex social structures involving multiple groups, such as multilevel societies. Drones made it possible to obtain the identification, accurate positioning, or movement of more than a hundred individuals in a multilevel social group. In addition, in multilevel social groups, drones facilitate the observation of heterogeneous spatial positioning patterns and mechanisms of behavioral propagation, which are different from those in a single-level group. Such findings may contribute to the quantitative definition and assessment of multilevel societies and enhance our understanding of mechanisms of multiple group aggregation. The application of drones to various species may resolve various questions related to multilevel societies.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Ontogeny of collective behaviour

During their lifetime, superorganisms, like unitary organisms, undergo transformations that change the machinery of their collective behaviour. Here, we suggest that these transformations are largely understudied and propose that more systematic research into the ontogeny of collective behaviours is needed if we hope to better understand the link between proximate behavioural mechanisms and the development of collective adaptive functions. In particular, certain social insects engage in self-assemblage, forming dynamic and physically connected architectures with striking similarities to developing multicellular organisms, making them good model systems for ontogenetic studies of collective behaviour. However, exhaustive time series and three-dimensional data are required to thoroughly characterize the different life stages of the collective structures and the transitions between these stages. The well-established fields of embryology and developmental biology offer practical tools and theoretical frameworks that could speed up the acquisition of new knowledge about the formation, development, maturity and dissolution of social insect self-assemblages and, by extension, other superorganismal behaviours. We hope that this review will encourage an expansion of the ontogenetic perspective in the field of collective behaviour and, in particular, in self-assemblage research, which has far-reaching applications in robotics, computer science and regenerative medicine. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.

Using machine learning to track dogs' exploratory behaviour in the presence and absence of their caregiver

Machine-learning-based behavioural tracking is a rapidly growing method in the behavioural sciences providing data with high spatial and temporal resolution while reducing the risk of observer bias. Nevertheless, only a few canine behaviour studies have applied this method. In the current study, we used three-dimensional (3D) tracking of the dogs' bodies to study how separation from the caregiver affected the dogs' behaviour in a novel environment. During the study, the dogs could move freely in a room equipped with trial-unique objects. We manipulated across trials whether the owner and/or a stranger was present in the room to evaluate the secure base effect, the tendency to explore and play more in the presence of the caregiver compared to another person. This secure base effect is considered a key characteristic of human attachment bonds and has also been described for the dog-caregiver relationship. The tracking data were internally consistent and highly correlated with human scorings and measurements. The results show that both the owner and stranger significantly increased the dogs' exploration; the dogs also spent more time in the proximity of the owner and stranger location when they were present. Even though the presence of both owner and stranger had a significant effect on the dogs' behaviour, the effect of the owner was more pronounced. Moreover, in the presence of the stranger the dogs spent more time close to their owner and showed a reduced tail-wagging asymmetry to the right side further supporting the distinct effect of owner and stranger on the dogs' behaviour. We conclude that machine-driven 3D tracking provides an efficient and reliable access for detailed behavioural analyses of dogs' exploration and attachment-related behaviours.

Assessing neophobia and exploration while accounting for social context: an example application in scimitar-horned oryx

Spatial neophobia and exploration are often assessed in nonhuman animals by measuring behavioral responses to novel environments. These traits may especially affect the performance of individuals translocated to novel environments for conservation purposes. Here, we present methods to administer and analyze a minimally invasive novel environment test that accounts for the social context of focal individuals. We used an aerial platform to capture video footage of a captive herd of scimitar-horned oryx (Oryx dammah) entering an unfamiliar enclosure. We analyzed footage for seven individually identifiable oryx, scoring their behavioral responses (i.e., latency to enter the enclosure, and movement and posture after entering the enclosure) and social context (i.e., relative position and number of nearby animals). We performed a principal components analysis (PCA) to explore individual traits and responses, and used generalized linear mixed models (GLMMs) to determine the effect of individual traits and social context on individual posture and movement behaviors. Both PCA and GLMMs supported our expectation that social context affects individual behavior: high neighbor density and relative position were negatively related to individual movement, and variation in social context was positively related with head-up postures and movement. Oryx were well differentiated along two principal components that reflected (1) vigilance or caution, and (2) changing social context and age. These methods provide a framework for assessing individual responses to a novel environment in a group setting, which can inform reintroduction and wildlife management efforts, while minimizing interference with animal behavior and management operations.

Novel form of collective movement by soil bacteria

Although migrations are essential for soil microorganisms to exploit scarce and heterogeneously distributed resources, bacterial mobility in soil remains poorly studied due to experimental limitations. In this study, time-lapse images collected using live microscopy techniques captured collective and coordinated groups of B. subtilis cells exhibiting "crowd movement". Groups of B. subtilis cells moved through transparent soil (nafion polymer with particle size resembling sand) toward plant roots and re-arranged dynamically around root tips in the form of elongating and retracting "flocks" resembling collective behaviour usually associated with higher organisms (e.g., bird flocks or fish schools). Genetic analysis reveals B. subtilis flocks are likely driven by the diffusion of extracellular signalling molecules (e.g., chemotaxis, quorum sensing) and may be impacted by the physical obstacles and hydrodynamics encountered in the soil like environment. Our findings advance understanding of bacterial migration through soil matrices and expand known behaviours for coordinated bacterial movement.

Video Validation of Tri-Axial Accelerometer for Monitoring Zoo-Housed Tamandua tetradactyla Activity Patterns in Response to Changes in Husbandry Conditions

Accelerometers are a technology that is increasingly used in the evaluation of animal behaviour. A tri-axial accelerometer attached to a vest was used on Tamandua tetradactyla individuals (n = 10) at Biodiversity Park. First, the influence of using a vest on the animals’ behaviour was evaluated (ABA-type: A1 and A2, without a vest; B, with a vest; each stage lasted 24 h), and no changes were detected. Second, their behaviour was monitored using videos and the accelerometer simultaneously (experimental room, 20 min). The observed behaviours were correlated with the accelerometer data, and summary measures (X, Y and Z axes) were obtained. Additionally, the overall dynamic body acceleration was calculated, determining a threshold to discriminate activity/inactivity events (variance = 0.0055). Then, based on a 24 h complementary test (video sampling every 5 min), the sensitivity (85.91%) and precision (100%) of the accelerometer were assessed. Animals were exposed to an ABA-type experimental design: A1 and A2: complex enclosure; B: decreased complexity (each stage lasted 24 h). An increase in total activity (%) was revealed using the accelerometer (26.15 ± 1.50, 29.29 ± 2.25, and 35.36 ± 3.15, respectively). Similar activity levels were detected using video analysis. The results demonstrate that the use of the accelerometer is reliable to determine the activity. Considering that the zoo-housed lesser anteaters exhibit a cathemeral activity pattern, this study contributes to easily monitoring their activities and responses to different management procedures supporting welfare programs, as well as ex situ conservation.

Advances in non-invasive tracking of wave-type electric fish in natural and laboratory settings

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g., video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with an array of 64 electrodes distributed over a 12 m² section of a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique model system for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.

Personality drives activity and space use in a mammalian herbivore

BACKGROUND

Animal personality has emerged as a key concept in behavioral ecology. While many studies have demonstrated the influence of personality traits on behavioral patterns, its quantification, especially in wild animal populations, remains a challenge. Only a few studies have established a link between personality and recurring movements within home ranges, although these small-scale movements are of key importance for identifying ecological interactions and forming individual niches. In this regard, differences in space use among individuals might reflect different exploration styles between behavioral types along the shy-bold continuum.

METHODS

We assessed among-individual differences in behavior in the European hare (Lepus europaeus), a characteristic mammalian herbivore in agricultural landscapes using a standardized box emergence test for captive and wild hares. We determined an individuals' degree of boldness by measuring the latencies of behavioral responses in repeated emergence tests in captivity. During capture events of wild hares, we conducted a single emergence test and recorded behavioral responses proven to be stable over time in captive hares. Applying repeated novel environment tests in a near-natural enclosure, we further quantified aspects of exploration and activity in captive hares. Finally, we investigated whether and how this among-individual behavioral variation is related to general activity and space use in a wild hare population. Wild and captive hares were treated similarly and GPS-collared with internal accelerometers prior to release to the wild or the outdoor enclosure, respectively. General activity was quantified as overall dynamic body acceleration (ODBA) obtained from accelerometers. Finally, we tested whether boldness explained variation in (i) ODBA in both settings and (ii) variation in home ranges and core areas across different time scales of GPS-collared hares in a wild population.

RESULTS

We found three behavioral responses to be consistent over time in captive hares. ODBA was positively related to boldness (i.e., short latencies to make first contact with the new environment) in both captive and wild hares. Space use in wild hares also varied with boldness, with shy individuals having smaller core areas and larger home ranges than bold conspecifics (yet in some of the parameter space, this association was just marginally significant).

CONCLUSIONS

Against our prediction, shy individuals occupied relatively large home ranges but with small core areas. We suggest that this space use pattern is due to them avoiding risky, and energy-demanding competition for valuable resources. Carefully validated, activity measurements (ODBA) from accelerometers provide a valuable tool to quantify aspects of animal personality along the shy-bold continuum remotely. Without directly observing-and possibly disturbing-focal individuals, this approach allows measuring variability in animal personality, especially in species that are difficult to assess with experiments. Considering that accelerometers are often already built into GPS units, we recommend activating them at least during the initial days of tracking to estimate individual variation in general activity and, if possible, match them with a simple novelty experiment. Furthermore, information on individual behavioral types will help to facilitate mechanistic understanding of processes that drive spatial and ecological dynamics in heterogeneous landscapes.

Vision and vocal communication guide three-dimensional spatial coordination of zebra finches during wind-tunnel flights

Animal collective motion is a natural phenomenon readily observable in various taxa. Although theoretical models can predict the macroscopic pattern of group movements based on the relative spatial position of group members, it is poorly understood how group members exchange directional information, which enables the spatial coordination between individuals during collective motion. To test if vocalizations emitted during flocking flight are used by birds to transmit directional information between group members, we recorded vocal behaviour, head orientation and spatial position of each individual in a small flock of zebra finches (Taeniopygia guttata) flying in a wind tunnel. We found that the finches can use both visual and acoustic cues for three-dimensional flock coordination. When visual information is insufficient, birds can increasingly exploit active vocal communication to avoid collisions with flock mates. Our study furthers the mechanistic understanding of collective motion in birds and highlights the impact interindividual vocal interactions can have on group performances in these animals.

Zebra finches flying in a wind tunnel use both vocal and visual communication to orientate themselves within the flock, and are able to enhance their use of one form of communication over another depending on circumstance.

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior

In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator’s attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested in robotico can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.

Social Determinants of Inter-Individual Variability and Vulnerability: The Role of Dopamine

Individuals differ in their traits and preferences, which shape their interactions, their prospects for survival and their susceptibility to diseases. These correlations are well documented, yet the neurophysiological mechanisms underlying the emergence of distinct personalities and their relation to vulnerability to diseases are poorly understood. Social ties, in particular, are thought to be major modulators of personality traits and psychiatric vulnerability, yet the majority of neuroscience studies are performed on rodents in socially impoverished conditions. Rodent micro-society paradigms are therefore key experimental paradigms to understand how social life generates diversity by shaping individual traits. Dopamine circuitry is implicated at the interface between social life experiences, the expression of essential traits, and the emergence of pathologies, thus proving a possible mechanism to link these three concepts at a neuromodulatory level. Evaluating inter-individual variability in automated social testing environments shows great promise for improving our understanding of the link between social life, personality, and precision psychiatry – as well as elucidating the underlying neurophysiological mechanisms.

Seasonal movements in caribou ecotypes of Western Canada

BACKGROUND

Several migratory ungulates, including caribou, are dramatically declining. Caribou of the Barren-ground ecotype, which forms its own subspecies, are known to be mainly migratory. By contrast, within the Woodland subspecies, animals of the Boreal ecotype are known to be mainly sedentary, while those within the Northern and Central Mountain ecotypes to be partially migratory, with only some individuals migrating. Promotion of conservation actions (e.g., habitat protection) that are specific to both residents and migrants, as well as to the areas they frequent seasonally (which may be separate for migrants), requires distinguishing migration from other movement behaviours, which might be a challenge.

METHODS

We aimed at assessing seasonal movement behaviours, including migratory, resident, dispersing, and nomadic, for caribou belonging to the Barren-ground and Woodland subspecies and ecotypes. We examined seasonal displacement, both planar and altitudinal, and seasonal ranges overlap for 366 individuals that were GPS-collared in Northern and Western Canada. Lastly, we assessed the ability of caribou individuals to switch between migratory and non-migratory movement behaviours between years.

RESULTS

We detected migratory behaviour within each of the studied subspecies and ecotypes. However, seasonal ranges overlap (an index of sedentary behaviour) varied, with proportions of clear migrants (0 overlap) of 40.94% for Barren-ground caribou and 23.34% for Woodland caribou, and of 32.95%, 54.87%, and 8.86% for its Northern Mountain, Central Mountain, and Boreal ecotype, respectively. Plastic switches of individuals were also detected between migratory, resident, dispersing, and nomadic seasonal movements performed across years.

CONCLUSIONS

Our unexpected findings of marked seasonal movement plasticity in caribou indicate that this phenomenon should be better studied to understand the resilience of this endangered species to habitat and climatic changes. Our results that a substantial proportion of individuals engaged in seasonal migration in all studied ecotypes indicate that caribou conservation plans should account for critical habitat in both summer and winter ranges. Accordingly, conservation strategies are being devised for the Woodland subspecies and its ecotypes, which were found to be at least partially migratory in this study. Our findings that migration is detectable with both planar and altitudinal analyses of seasonal displacement provide a tool to better define seasonal ranges, also in mountainous and hilly environments, and protect habitat there.

Social networks and the conservation of fish

Despite our critical dependence on aquatic wildlife, we lack a complete understanding of the drivers of population stability and structure for most fish species. Social network analysis has been increasingly used to investigate animal societies as it explicitly links individual decision-making to population-level processes and demography. While the study of social structure is of great ecological interest, it is also potentially important for species of economic value or of conservation concern. To date however, there has been little focus on how social processes are likely to influence the conservation of fish populations. Here we identify applications for how a social network approach can help address broad fish conservation themes such as population structure, biological invasions or fisheries management. We discuss the burgeoning opportunities offered and challenges still faced by current technologies to integrate social network approaches within fish conservation.

Perspectives in machine learning for wildlife conservation

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

Flexible group cohesion and coordination, but robust leader-follower roles, in a wild social primate using urban space

Collective behaviour has a critical influence on group social structure and organization, individual fitness and social evolution, but we know little about whether and how it changes in anthropogenic environments. Here, we show multiple and varying effects of urban space-use upon group-level processes in a primate generalist-the chacma baboon (Papio ursinus)-within a managed wild population living at the urban edge in the City of Cape Town, South Africa. In natural space, we observe baboon-typical patterns of collective behaviour. By contrast, in urban space (where there are increased risks, but increased potential for high-quality food rewards), baboons show extreme flexibility in collective behaviour, with changes in spatial cohesion and association networks, travel speeds and group coordination. However, leader-follower roles remain robust across natural and urban space, with adult males having a disproportionate influence on the movement of group members. Their important role in the group's collective behaviour complements existing research and supports the management tactic employed by field rangers of curbing the movements of adult males, which indirectly deters the majority of the group from urban space. Our findings highlight both flexibility and robustness in collective behaviour when groups are presented with novel resources and heightened risks.

Quantifying song behavior in a free-living, light-weight, mobile bird using accelerometers

To acquire a fundamental understanding of animal communication, continuous observations in a natural setting and at an individual level are required. Whereas the use of animal-borne acoustic recorders in vocal studies remains challenging, light-weight accelerometers can potentially register individuals' vocal output when this coincides with body vibrations. We collected one-dimensional accelerometer data using light-weight tags on a free-living, crepuscular bird species, the European Nightjar (Caprimulgus europaeus). We developed a classification model to identify four behaviors (rest, sing, fly, and leap) from accelerometer data and, for the purpose of this study, validated the classification of song behavior. Male nightjars produce a distinctive "churring" song while they rest on a stationary song post. We expected churring to be associated with body vibrations (i.e., medium-amplitude body acceleration), which we assumed would be easy to distinguish from resting (i.e., low-amplitude body acceleration). We validated the classification of song behavior using simultaneous GPS tracking data (i.e., information on individuals' movement and proximity to audio recorders) and vocal recordings from stationary audio recorders at known song posts of one tracked individual. Song activity was detected by the classification model with an accuracy of 92%. Beyond a threshold of 20 m from the audio recorders, only 8% of the classified song bouts were recorded. The duration of the detected song activity (i.e., acceleration data) was highly correlated with the duration of the simultaneously recorded song bouts (correlation coefficient = 0.87, N = 10, S = 21.7, p = .001). We show that accelerometer-based identification of vocalizations could serve as a promising tool to study communication in free-living, small-sized birds and demonstrate possible limitations of audio recorders to investigate individual-based variation in song behavior.

Habituation of common vampire bats to biologgers

Rapid advancements in biologging technology have led to unprecedented insights into animal behaviour, but testing the effects of biologgers on tagged animals is necessary for both scientific and ethical reasons. Here, we measured how quickly 13 wild-caught and captively isolated common vampire bats (Desmodus rotundus) habituated to mock proximity sensors glued to their dorsal fur. To assess habituation, we scored video-recorded behaviours every minute from 18.00 to 06.00 for 3 days, then compared the rates of grooming directed to the sensor tag versus to their own body. During the first hour, the mean tag-grooming rate declined dramatically from 53% of sampled time (95% CI = 36-65%, n = 6) to 16% (8-24%, n = 9), and down to 4% by hour 5 (1-6%, n = 13), while grooming of the bat's own body did not decline. When tags are firmly attached, isolated individual vampire bats mostly habituate within an hour of tag attachment. In two cases, however, tags became loose before falling off causing the bats to dishabituate. For tags glued to fur, behavioural data are likely to be impacted immediately after the tag is attached and when it is loose before it falls off.

Socioecology Explains Individual Variation in Urban Space Use in Response to Management in Cape Chacma Baboons (Papio ursinus)

The presence of wildlife adjacent to and within urban spaces is a growing phenomenon globally. When wildlife’s presence in urban spaces has negative impacts for people and wildlife, nonlethal and lethal interventions on animals invariably result. Recent evidence suggests that individuals in wild animal populations vary in both their propensity to use urban space and their response to nonlethal management methods. Understanding such interindividual differences and the drivers of urban space use could help inform management strategies. We use direct observation and high-resolution GPS (1 Hz) to track the space use of 13 adult individuals in a group of chacma baboons (Papio ursinus) living at the urban edge in Cape Town, South Africa. The group is managed by a dedicated team of field rangers, who use aversive conditioning to reduce the time spent by the group in urban spaces. Adult males are larger, more assertive, and more inclined to enter houses, and as such are disproportionately subject to “last resort” lethal management. Field rangers therefore focus efforts on curbing the movements of adult males, which, together with high-ranking females and their offspring, comprise the bulk of the group. However, our results reveal that this focus allows low-ranking, socially peripheral female baboons greater access to urban spaces. We suggest that movement of these females into urban spaces, alone or in small groups, is an adaptive response to management interventions, especially given that they have no natural predators. These results highlight the importance of conducting behavioral studies in conjunction with wildlife management, to ensure effective mitigation techniques.

How often should dead-reckoned animal movement paths be corrected for drift?

BACKGROUND

Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, 'GPS') is typically used to verify an animal's location periodically. Straight lines are typically drawn between these 'Verified Positions' ('VPs') so the interpolation of space-use is limited by the temporal and spatial resolution of the system's measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.

METHODS AND RESULTS

Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.

CONCLUSIONS

We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

Video Recording and Analysis of Avian Movements and Behavior: Insights from Courtship Case Studies

Video recordings are useful tools for advancing our understanding of animal movements and behavior. Over the past decades, a burgeoning area of behavioral research has put forward innovative methods to investigate animal movement using video analysis, which includes motion capture and machine learning algorithms. These tools are particularly valuable for the study of elaborate and complex motor behaviors, but can be challenging to use. We focus in particular on elaborate courtship displays, which commonly involve rapid and/or subtle motor patterns. Here, we review currently available tools and provide hands-on guidelines for implementing these techniques in the study of avian model species. First, we suggest a set of possible strategies and solutions for video acquisition based on different model systems, environmental conditions, and time or financial budget. We then outline the available options for video analysis and illustrate how different analytical tools can be chosen to draw inference about animal motor performance. Finally, a detailed case study describes how these guidelines have been implemented to study courtship behavior in golden-collared manakins (Manacus vitellinus).

Collective Emotional Contagion in Zebrafish

Seeking to match our emotional state with one of those around us is known as emotional contagion-a fundamental biological process that underlies social behavior across several species and taxa. While emotional contagion has been traditionally considered to be a prerogative of mammals and birds, recent findings are demonstrating otherwise. Here, we investigate emotional contagion in groups of zebrafish, a freshwater model species which is gaining momentum in preclinical studies. Zebrafish have high genetic homology to humans, and they exhibit a complex behavioral repertoire amenable to study social behavior. To investigate whether individual emotional states can be transmitted to group members, we pharmacologically modulated anxiety-related behaviors of a single fish through Citalopram administration and we assessed whether the altered emotional state spread to a group of four untreated conspecifics. By capitalizing upon our in-house developed tracking algorithm, we successfully preserved the identity of all the subjects and thoroughly described their individual and social behavioral phenotypes. In accordance with our predictions, we observed that Citalopram administration consistently reduced behavioral anxiety of the treated individual, in the form of reduced geotaxis, and that such a behavioral pattern readily generalized to the untreated subjects. A transfer entropy analysis of causal interactions within the group revealed that emotional contagion was directional, whereby the treated individual influenced untreated subjects, but not vice-versa. This study offers additional evidence that emotional contagion is biologically preserved in simpler living organisms amenable to preclinical investigations.

Steps to build a DIY low-cost fixed-wing drone for biodiversity conservation

Despite the proved usefulness of drones in biodiversity studies, acquisition costs and difficulties in operating, maintaining and repairing these systems constrain their integration in conservation projects, particularly for low-income countries. Here we present the steps necessary to build a low-cost fixed-wing drone for environmental applications in large areas, along with instructions to increase the reliability of the system and testing its performance. Inspired by DIY (Do It Yourself) and open source models, this work prioritizes simplicity and accounts for cost-benefit for the researcher. The DIY fixed-wing drone developed has electric propulsion, can perform pre-programmed flight, can carry up to 500 g payload capacity with 65 minutes flight duration and flies at a maximum distance of 20 km. It is equipped with a RGB (Red, Green and Blue) sensor capable of obtaining 2.8 cm per pixel Ground Sample Distance (GSD) resolution at a constant altitude of 100 m above ground level (AGL). The total cost was $995 which is substantially less than the average value of similar commercial drones used in biodiversity studies. We performed 12 flight tests in auto mode using the developed model in protected areas in Brazil, obtaining RGB images that allowed us to identify deforestation spots smaller than 5 m² and medium-sized animals. Building DIY drones requires some technical knowledge and demands more time than buying a commercial ready-to-fly system, but as proved here, it can be less expensive, which is often crucial in conservation projects.

On the move: sloths and their epibionts as model mobile ecosystems

Sloths are unusual mobile ecosystems, containing a high diversity of epibionts living and growing in their fur as they climb slowly through the canopies of tropical forests. These epibionts include poorly studied algae, arthropods, fungi, and bacteria, making sloths likely reservoirs of unexplored biodiversity. This review aims to identify gaps and eliminate misconceptions in our knowledge of sloths and their epibionts, and to identify key questions to stimulate future research into the functions and roles of sloths within a broader ecological and evolutionary context. This review also seeks to position the sloth fur ecosystem as a model for addressing fundamental questions in metacommunity and movement ecology. The conceptual and evidence-based foundation of this review aims to serve as a guide for future hypothesis-driven research into sloths, their microbiota, sloth health and conservation, and the coevolution of symbioses in general.

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

Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60 Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5 and 46.7 times faster, and requires 2–10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.

Behaviour influences thermoregulation of boreal moose during the warm season

Management of large herbivores depends on providing habitats for forage supply and refuge from risks of temperature, predation and disease. Moose (Alces alces) accumulate body energy and nutrient stores during summer, while reducing the impact of warm temperatures through physiological and behavioural thermoregulation. Building on the animal indicator concept, we used rumen temperature sensors and GPS collars on captive moose (n = 6) kept in large natural enclosures to evaluate how behaviour and habitat selection influence the rate of change in rumen temperature during the growing season on the Kenai Peninsula, Alaska, USA. We compared movement and habitat selection of individual females during tolerance days (daily amplitude in rumen temperature was ≥1.2°C in 24 h) with those of control days (daily amplitude in rumen temperature was < 1.2°C) before and after the tolerance day. Moose moved more during tolerance days (172 m • h-1; 95% confidence intervals (CI)  = 149-191 m • h-1) than on control days (151 m • h-1; 95% CI = 128-173 m • h-1). The rate of change in rumen temperature (°C • h-1) declined with low to moderate movement rates that were probably associated with foraging in all habitats. Movement only increased the rate of change in rumen temperature at high activity (~ > 500 m • h-1). Additionally, the relationship between rate of change in rumen temperature and movement rate was different during tolerance and control days in open meadow and wetland habitats. In all habitats except wetlands, the rate of change in rumen temperature increased while resting, which probably is a result of diet-induced thermogenesis. Our study demonstrates that the behavioural choices of moose on the landscape are associated with the rate of change in rumen temperature and their ability to thermoregulate. Wildlife managers must consider high-value habitats where wildlife can employ both behavioural and physiological mechanisms to tolerate warm ambient conditions in a landscape of forage, predators and pests.

Automated measurement of long-term bower behaviors in Lake Malawi cichlids using depth sensing and action recognition

In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. By registering these two data streams, we show that behaviors can be topographically mapped onto a dynamic 3D sand surface through time. The system runs reliably in multiple species, across many aquariums simultaneously, and for up to weeks at a time. Using this system, we show strong differences in construction behavior and bower form that reflect species differences in nature, and we gain new insights into spatial, temporal, social dimensions of bower construction, feeding, and quivering behaviors. Taken together, our work highlights how low-cost tools can automatically quantify behavior in naturalistic and social environments over long timescales in the lab.

Collective navigation can facilitate passage through human-made barriers by homeward migrating Pacific salmon

The mass migration of animals is one of the great wonders of the natural world. Although there are multiple benefits for individuals migrating in groups, an increasingly recognized benefit is collective navigation, whereby social interactions improve animals’ ability to find their way. Despite substantial evidence from theory and laboratory-based experiments, empirical evidence of collective navigation in nature remains sparse. Here we used a unique large-scale radiotelemetry dataset to analyse the movements of adult Pacific salmon (Oncorhynchus sp.) in the Columbia River Basin, USA. These salmon face substantial migratory challenges approaching, entering and transiting fishways at multiple large-scale hydroelectric mainstem dams. We assess the potential role of collective navigation in overcoming these challenges and show that Chinook salmon (O. tshawytscha), but not sockeye salmon (O. nerka) locate fishways faster and pass in fewer attempts at higher densities, consistent with collective navigation. The magnitude of the density effects were comparable to major established drivers such as water temperature, and model simulations predicted that major fluctuations in population density can have substantial impacts on key quantities including mean passage time and fraction of fish with very long passage times. The magnitude of these effects indicates the importance of incorporating conspecific density and social dynamics into models of the migration process. Density effects on both ability to locate fishways and number of passage attempts have the potential to enrich our understanding of migratory energetics and success of migrating anadromous salmonids. More broadly, our work reveals a potential role of collective navigation, in at least one species, to mitigate the effects of anthropogenic barriers to animals on the move.

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement

BACKGROUND

Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.

METHODS

We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.

RESULTS

We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows' neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.

CONCLUSIONS

Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

SUPPLEMENTARY INFORMATION

Supplementary information accompanies this paper at 10.1186/s40462-020-00228-4.