From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

Unraveling clonal CD8 T cell expansion and identification of essential factors in γ-herpesvirus-induced lymphomagenesis

Alcelaphine gammaherpesvirus 1 (AlHV-1) asymptomatically persists in its natural host, the wildebeest. However, cross-species transmission to cattle results in the induction of an acute and lethal peripheral T cell lymphoma-like disease (PTCL), named malignant catarrhal fever (MCF). Our previous findings demonstrated an essential role for viral genome maintenance in infected CD8+ T lymphocytes but the exact mechanism(s) leading to lymphoproliferation and MCF remained unknown. To decipher how AlHV-1 dysregulates T lymphocytes, we first examined the global phenotypic changes in circulating CD8+ T cells after experimental infection of calves. T cell receptor repertoire together with transcriptomics and epigenomics analyses demonstrated an oligoclonal expansion of infected CD8+ T cells displaying effector and exhaustion gene signatures, including GZMA, GNLY, PD-1, and TOX2 expression. Then, among viral genes expressed in infected CD8+ T cells, we uncovered A10 that encodes a transmembrane signaling protein displaying multiple tyrosine residues, with predicted ITAM and SH3 motifs. Impaired A10 expression did not affect AlHV-1 replication in vitro but rendered AlHV-1 unable to induce MCF. Furthermore, A10 was phosphorylated in T lymphocytes in vitro and affected T cell signaling. Finally, while AlHV-1 mutants expressing mutated forms of A10 devoid of ITAM or SH3 motifs (or both) were able to induce MCF, a recombinant virus expressing a mutated form of A10 unable to phosphorylate its tyrosine residues resulted in the lack of MCF and protected against a wild-type virus challenge. Thus, we could characterize the nature of this γ-herpesvirus-induced PTCL-like disease and identify an essential mechanism explaining its development.

Deep vs shallow: GPS tags reveal a dichotomy in movement patterns of loggerhead turtles foraging in a coastal bay

BACKGROUND

Individual variation in movement strategies of foraging loggerhead turtles have been documented on the scale of tens to hundreds of kilometers within single ocean basins. Use of different strategies among individuals may reflect variations in resources, predation pressure or competition. It is less common for individual turtles to use different foraging strategies on the scale of kilometers within a single coastal bay. We used GPS tags capable of back-filling fine-scale locations to document movement patterns of loggerhead turtles in a coastal bay in Northwest Florida, U.S.A.

METHODS

Iridium-linked GPS tags were deployed on loggerhead turtles at a neritic foraging site in Northwest Florida. After filtering telemetry data, point locations were transformed to movement lines and then merged with the original point file to define travel paths and assess travel speed. Home ranges were determined using kernel density function. Diurnal behavioral shifts were examined by examining turtle movements compared to solar time.

RESULTS

Of the 11 turtles tagged, three tracked turtles remained in deep (~ 6 m) water for almost the entire tracking period, while all other turtles undertook movements from deep water locations, located along edges and channels, to shallow (~ 1-2 m) shoals at regular intervals and primarily at night. Three individuals made short-term movements into the Gulf of Mexico when water temperatures dropped, and movement speeds in the Gulf were greater than those in the bay. Turtles exhibited a novel behavior we termed drifting.

CONCLUSIONS

This study highlighted the value provided to fine-scale movement studies for species such as sea turtles that surface infrequently by the ability of these GPS tags to store and re-upload data. Future use of these tags at other loggerhead foraging sites, and concurrent with diving and foraging data, would provide a powerful tool to better understand fine-scale movement patterns of sea turtles.

Representing stimulus motion with waves in adaptive neural fields

Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.

Exploring the interplay between small and large scales movements in a neotropical small mammal

We record and analyze the movement patterns of the marsupial Didelphis aurita at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques and, with the help of radio-tracking devices, animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated Lévy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated Lévy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of 1 week. These results show that whether Lévy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, Lévy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.

Anisotropic interaction and motion states of locusts in a hopper band

Swarming locusts present a quintessential example of animal collective motion. Juvenile locusts march and hop across the ground in coordinated groups called hopper bands. Composed of up to millions of insects, hopper bands exhibit aligned motion and various collective structures. These groups are well-documented in the field, but the individual insects themselves are typically studied in much smaller groups in laboratory experiments. We present, to our knowledge, the first trajectory data that detail the movement of individual locusts within a hopper band in a natural setting. Using automated video tracking, we derive our data from footage of four distinct hopper bands of the Australian plague locust, Chortoicetes terminifera. We reconstruct nearly 200 000 individual trajectories composed of over 3.3 million locust positions. We classify these data into three motion states: stationary, walking and hopping. Distributions of relative neighbour positions reveal anisotropies that depend on motion state. Stationary locusts have high-density areas distributed around them apparently at random. Walking locusts have a low-density area in front of them. Hopping locusts have low-density areas in front and behind them. Our results suggest novel insect interactions, namely that locusts change their motion to avoid colliding with neighbours in front of them.

Representing stimulus motion with waves in adaptive neural fields

Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.

Learning from algorithm-generated pseudo-annotations for detecting ants in videos

Deep learning (DL) based detection models are powerful tools for large-scale analysis of dynamic biological behaviors in video data. Supervised training of a DL detection model often requires a large amount of manually-labeled training data which are time-consuming and labor-intensive to acquire. In this paper, we propose LFAGPA (Learn From Algorithm-Generated Pseudo-Annotations) that utilizes (noisy) annotations which are automatically generated by algorithms to train DL models for ant detection in videos. Our method consists of two main steps: (1) generate foreground objects using a (set of) state-of-the-art foreground extraction algorithm(s); (2) treat the results from step (1) as pseudo-annotations and use them to train deep neural networks for ant detection. We tackle several challenges on how to make use of automatically generated noisy annotations, how to learn from multiple annotation resources, and how to combine algorithm-generated annotations with human-labeled annotations (when available) for this learning framework. In experiments, we evaluate our method using 82 videos (totally 20,348 image frames) captured under natural conditions in a tropical rain-forest for dynamic ant behavior study. Without any manual annotation cost but only algorithm-generated annotations, our method can achieve a decent detection performance (77% in [Formula: see text] score). Moreover, when using only 10% manual annotations, our method can train a DL model to perform as well as using the full human annotations (81% in [Formula: see text] score).

Identifying the social context of single- and mixed-species group formation in large African herbivores

Despite continued interest in mixed-species groups, we still lack a unified understanding of how ecological and social processes work across scales to influence group formation. Recent work has revealed ecological correlates of mixed-species group formation, but the mechanisms by which concomitant social dynamics produce these patterns, if at all, is unknown. Here, we use camera trap data for six mammalian grazer species in Serengeti National Park. Building on previous work, we found that ecological variables, and especially forage quality, influenced the chances of species overlap over small spatio-temporal scales (i.e. on the scales of several metres and hours). Migratory species (gazelle, wildebeest and zebra) were more likely to have heterospecific partners available in sites with higher forage quality, but the opposite was true for resident species (buffalo, hartebeest and topi). These findings illuminate the circumstances under which mixed-species group formation is even possible. Next, we found that greater heterospecific availability was associated with an increased probability of mixed-species group formation in gazelle, hartebeest, wildebeest and zebra, but ecological variables did not further shape these patterns. Overall, our results are consistent with a model whereby ecological and social drivers of group formation are species-specific and operate on different spatio-temporal scales. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.

Dynamics of collective motion across time and species

Most studies of collective animal behaviour rely on short-term observations, and comparisons of collective behaviour across different species and contexts are rare. We therefore have a limited understanding of intra- and interspecific variation in collective behaviour over time, which is crucial if we are to understand the ecological and evolutionary processes that shape collective behaviour. Here, we study the collective motion of four species: shoals of stickleback fish (Gasterosteus aculeatus), flocks of homing pigeons (Columba livia), a herd of goats (Capra aegagrus hircus) and a troop of chacma baboons (Papio ursinus). First, we describe how local patterns (inter-neighbour distances and positions), and group patterns (group shape, speed and polarization) during collective motion differ across each system. Based on these, we place data from each species within a 'swarm space', affording comparisons and generating predictions about the collective motion across species and contexts. We encourage researchers to add their own data to update the 'swarm space' for future comparative work. Second, we investigate intraspecific variation in collective motion over time and provide guidance for researchers on when observations made over different time scales can result in confident inferences regarding species collective motion. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic data set and then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.

Variation in movement strategies: Capital versus income migration

Animal migrations represent the regular movements of trillions of individuals. The scale of these movements has inspired human intrigue for millennia and has been intensively studied by biologists. This research has highlighted the diversity of migratory strategies seen across and within migratory taxa: while some migrants temporarily express phenotypes dedicated to travel, others show little or no phenotypic flexibility in association with migration. However, a vocabulary for describing these contrasting solutions to the performance trade-offs inherent to the highly dynamic lifestyle of migrants (and strategies intermediate between these two extremes) is currently missing. We propose a taxon-independent organising framework based on energetics, distinguishing between migrants that forage as they travel (income migrants) and those that fuel migration using energy acquired before departure (capital migrants). Not only does our capital:income continuum of migratory energetics account for the variable extent of phenotypic flexibility within and across migrant populations, but it also aligns with theoreticians' treatment of migration and clarifies how migration impacts other phases of the life cycle. As such, it provides a unifying scale and common vacabulary for comparing the migratory strategies of divergent taxa.

Spatial patterns in ecological systems: from microbial colonies to landscapes

Self-organized spatial patterns are ubiquitous in ecological systems and allow populations to adopt non-trivial spatial distributions starting from disordered configurations. These patterns form due to diverse nonlinear interactions among organisms and between organisms and their environment, and lead to the emergence of new (eco)system-level properties unique to self-organized systems. Such pattern consequences include higher resilience and resistance to environmental changes, abrupt ecosystem collapse, hysteresis loops, and reversal of competitive exclusion. Here, we review ecological systems exhibiting self-organized patterns. We establish two broad pattern categories depending on whether the self-organizing process is primarily driven by nonlinear density-dependent demographic rates or by nonlinear density-dependent movement. Using this organization, we examine a wide range of observational scales, from microbial colonies to whole ecosystems, and discuss the mechanisms hypothesized to underlie observed patterns and their system-level consequences. For each example, we review both the empirical evidence and the existing theoretical frameworks developed to identify the causes and consequences of patterning. Finally, we trace qualitative similarities across systems and propose possible ways of developing a more quantitative understanding of how self-organization operates across systems and observational scales in ecology.

The macroscopic limit to synchronization of cellular clocks in single cells of Neurospora crassa

We determined the macroscopic limit for phase synchronization of cellular clocks in an artificial tissue created by a “big chamber” microfluidic device to be about 150,000 cells or less. The dimensions of the microfluidic chamber allowed us to calculate an upper limit on the radius of a hypothesized quorum sensing signal molecule of 13.05 nm using a diffusion approximation for signal travel within the device. The use of a second microwell microfluidic device allowed the refinement of the macroscopic limit to a cell density of 2166 cells per fixed area of the device for phase synchronization. The measurement of averages over single cell trajectories in the microwell device supported a deterministic quorum sensing model identified by ensemble methods for clock phase synchronization. A strong inference framework was used to test the communication mechanism in phase synchronization of quorum sensing versus cell-to-cell contact, suggesting support for quorum sensing. Further evidence came from showing phase synchronization was density-dependent.

Big-data approaches lead to an increased understanding of the ecology of animal movement

Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.

African forest elephant movements depend on time scale and individual behavior

The critically endangered African forest elephant (Loxodonta cyclotis) plays a vital role in maintaining the structure and composition of Afrotropical forests, but basic information is lacking regarding the drivers of elephant movement and behavior at landscape scales. We use GPS location data from 96 individuals throughout Gabon to determine how five movement behaviors vary at different scales, how they are influenced by anthropogenic and environmental covariates, and to assess evidence for behavioral syndromes-elephants which share suites of similar movement traits. Elephants show some evidence of behavioral syndromes along an 'idler' to 'explorer' axis-individuals that move more have larger home ranges and engage in more 'exploratory' movements. However, within these groups, forest elephants express remarkable inter-individual variation in movement behaviours. This variation highlights that no two elephants are the same and creates challenges for practitioners aiming to design conservation initiatives.

A hierarchical machine learning framework for the analysis of large scale animal movement data

BACKGROUND

In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated.

METHODS

In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods.

RESULTS

While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries.

CONCLUSIONS

Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.

Seasonal niche tracking of climate emerges at the population level in a migratory bird

Seasonal animal migration is a widespread phenomenon. At the species level, it has been shown that many migratory animal species track similar climatic conditions throughout the year. However, it remains unclear whether such a niche tracking pattern is a direct consequence of individual behaviour or emerges at the population or species level through behavioural variability. Here, we estimated seasonal niche overlap and seasonal niche tracking at the individual and population level of central European white storks (Ciconia ciconia). We quantified niche tracking for both weather and climate conditions to control for the different spatio-temporal scales over which ecological processes may operate. Our results indicate that niche tracking is a bottom-up process. Individuals mainly track weather conditions while climatic niche tracking mainly emerges at the population level. This result may be partially explained by a high degree of intra- and inter-individual variation in niche overlap between seasons. Understanding how migratory individuals, populations and species respond to seasonal environments is key for anticipating the impacts of global environmental changes.

Crop raiders in an ecological trap: optimal foraging individual-based modeling quantifies the effect of alternate crops

Crop raiding is an increasing source of human-wildlife conflict that antagonizes humans and can lead to heightened killing of wildlife. Attraction to crops can trigger ecological traps, where animals prefer areas of their range that confer relatively low fitness. Food can be used to draw animals away from problematic areas, but an alternative considered less often is to replace high-quality food with poorer alternatives. In any case, managers often have no means of anticipating by how much such interventions should impact animal use of space. Optimal foraging theory predicts that foragers optimizing their diet should choose food items according to their relative profitability (i.e., digestible energy/ handling time), a theoretical prediction that can orient management actions. Accordingly, we developed an individual-based model (IBM) simulating movement through empirical rules under an optimal foraging framework. Our objective was to quantify the effect size of cultivating alternate crops to reduce crop raiding and the associated human-induced mortality driving an ecological trap for an energy maximizer, plains bison (Bison bison bison). Results showed that almost tripling the area of cultivation of crops of lower profitability (from 24.3% of the bison range outside the protected area in one management scenario to 70.3% in another) only led to a 25% additional decrease in the intensity of crop raiding (from a decrease of 40% in the first scenario to a decrease of 65% in the second). This suggests that localized interventions in the landscape are likely to have a stronger impact in mitigating crop raiding than broad actions ignoring spatial patterns in food distribution. However, we obtained no significant reduction in the number of simulated bison being harvested in the first scenario, and only a small reduction in the second, when the intervention was spatially broad. Our individual-based approach to animal movement informed by optimal foraging demonstrates that linking landscape configuration to mortality rates can help managers anticipate the effectiveness of manipulating food to keep animals away from problematic zones. Yet disarming ecological traps driven by human hunting appears to be a much more challenging undertaking.

Longest terrestrial migrations and movements around the world

Long-distance terrestrial migrations are imperiled globally. We determined both round-trip migration distances (straight-line measurements between migratory end points) and total annual movement (sum of the distances between successive relocations over a year) for a suite of large mammals that had potential for long-distance movements to test which species displayed the longest of both. We found that caribou likely do exhibit the longest terrestrial migrations on the planet, but, over the course of a year, gray wolves move the most. Our results were consistent with the trophic-level based hypothesis that predators would move more than their prey. Herbivores in low productivity environments moved more than herbivores in more productive habitats. We also found that larger members of the same guild moved less than smaller members, supporting the ‘gastro-centric’ hypothesis. A better understanding of migration and movements of large mammals should aid in their conservation by helping delineate conservation area boundaries and determine priority corridors for protection to preserve connectivity. The magnitude of the migrations and movements we documented should also provide guidance on the scale of conservation efforts required and assist conservation planning across agency and even national boundaries.

Optimizing the use of biologgers for movement ecology research

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.

Beyond Migration: Causes and Consequences of Nomadic Animal Movements

Recent advances in animal tracking reveal that many species display irregular movements that do not fall into classical categories of movement patterns such as range residency or migration. Here, we develop a unifying framework that distinguishes these nomadic movements based on their patterns, drivers, and mechanisms. Though they occur in diverse taxa and geographic regions, nomadic movements are united by both their underlying environmental drivers, mainly environmental stochasticity, and the resulting irregular, far-ranging movement patterns. The framework further classifies types of nomadic movements, including full, seasonal, phase, irruptive, and partial nomadism. Nomadic movements can have unique effects on populations, communities, and ecosystems, most notably providing intermittent disturbances and novel introductions of propagules.

Inferring the rules of social interaction in migrating caribou

Social interactions are a significant factor that influence the decision-making of species ranging from humans to bacteria. In the context of animal migration, social interactions may lead to improved decision-making, greater ability to respond to environmental cues, and the cultural transmission of optimal routes. Despite their significance, the precise nature of social interactions in migrating species remains largely unknown. Here we deploy unmanned aerial systems to collect aerial footage of caribou as they undertake their migration from Victoria Island to mainland Canada. Through a Bayesian analysis of trajectories we reveal the fine-scale interaction rules of migrating caribou and show they are attracted to one another and copy directional choices of neighbours, but do not interact through clearly defined metric or topological interaction ranges. By explicitly considering the role of social information on movement decisions we construct a map of near neighbour influence that quantifies the nature of information flow in these herds. These results will inform more realistic, mechanism-based models of migration in caribou and other social ungulates, leading to better predictions of spatial use patterns and responses to changing environmental conditions. Moreover, we anticipate that the protocol we developed here will be broadly applicable to study social behaviour in a wide range of migratory and non-migratory taxa.

This article is part of the theme issue ‘Collective movement ecology’.

Collective animal navigation and migratory culture: from theoretical models to empirical evidence

Animals often travel in groups, and their navigational decisions can be influenced by social interactions. Both theory and empirical observations suggest that such collective navigation can result in individuals improving their ability to find their way and could be one of the key benefits of sociality for these species. Here, we provide an overview of the potential mechanisms underlying collective navigation, review the known, and supposed, empirical evidence for such behaviour and highlight interesting directions for future research. We further explore how both social and collective learning during group navigation could lead to the accumulation of knowledge at the population level, resulting in the emergence of migratory culture.

This article is part of the theme issue ‘Collective movement ecology’.

Fitness trade-offs of group formation and movement by Thomson's gazelles in the Serengeti ecosystem

Collective behaviours contributing to patterns of group formation and coordinated movement are common across many ecosystems and taxa. Their ubiquity is presumably due to altering interactions between individuals and their predators, resources and physical environment in ways that enhance individual fitness. On the other hand, fitness costs are also often associated with group formation. Modifications to these interactions have the potential to dramatically impact population-level processes, such as trophic interactions or patterns of space use in relation to abiotic environmental variation. In a wide variety of empirical systems and models, collective behaviour has been shown to enhance access to ephemeral patches of resources, reduce the risk of predation and reduce vulnerability to environmental fluctuation. Evolution of collective behaviour should accordingly depend on the advantages of collective behaviour weighed against the costs experienced at the individual level. As an illustrative case study, we consider the potential trade-offs on Malthusian fitness associated with patterns of group formation and movement by migratory Thomson's gazelles in the Serengeti ecosystem.

This article is part of the theme issue ‘Collective movement ecology’.

Challenges and solutions for studying collective animal behaviour in the wild

Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.This article is part of the theme issue 'Collective movement ecology'.

Collective movement in ecology: from emerging technologies to conservation and management

Recent advances in technology and quantitative methods have led to the emergence of a new field of study that stands to link insights of researchers from two closely related, but often disconnected disciplines: movement ecology and collective animal behaviour. To date, the field of movement ecology has focused on elucidating the internal and external drivers of animal movement and the influence of movement on broader ecological processes. Typically, tracking and/or remote sensing technology is employed to study individual animals in natural conditions. By contrast, the field of collective behaviour has quantified the significant role social interactions play in the decision-making of animals within groups and, to date, has predominantly relied on controlled laboratory-based studies and theoretical models owing to the constraints of studying interacting animals in the field. This themed issue is intended to formalize the burgeoning field of collective movement ecology which integrates research from both movement ecology and collective behaviour. In this introductory paper, we set the stage for the issue by briefly examining the approaches and current status of research in these areas. Next, we outline the structure of the theme issue and describe the obstacles collective movement researchers face, from data acquisition in the field to analysis and problems of scale, and highlight the key contributions of the assembled papers. We finish by presenting research that links individual and broad-scale ecological and evolutionary processes to collective movement, and finally relate these concepts to emerging challenges for the management and conservation of animals on the move in a world that is increasingly impacted by human activity.This article is part of the theme issue 'Collective movement ecology'.