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.

Pathogen spillover during land conversion.

Pathogen spillover from wildlife to domestic animals and humans, and the reverse, has caused significant epidemics and pandemics worldwide. Although pathogen emergence has been linked to anthropogenic land conversion, a general framework to disentangle underlying processes is lacking. We develop a multi-host model for pathogen transmission between species inhabiting intact and converted habitat. Interspecies contacts and host populations vary with the proportion of land converted; enabling us to quantify infection risk across a changing landscape. In a range of scenarios, the highest spillover risk occurs at intermediate levels of habitat loss, whereas the largest, but rarest, epidemics occur at extremes of land conversion. This framework provides insights into the mechanisms driving disease emergence and spillover during land conversion. The finding that the risk of spillover is highest at intermediate levels of habitat loss provides important guidance for conservation and public health policy.

Specialization and evolutionary branching within migratory populations.

Understanding the mechanisms that drive specialization and speciation within initially homogeneous populations is a fundamental challenge for evolutionary theory. It is an issue of relevance for significant open questions in biology concerning the generation and maintenance of biodiversity, the origins of reciprocal cooperation, and the efficient division of labor in social or colonial organisms. Several mathematical frameworks have been developed to address this question and models based on evolutionary game theory or the adaptive dynamics of phenotypic mutation have demonstrated the emergence of polymorphic, specialized populations. Here we focus on a ubiquitous biological phenomenon, migration. Individuals in our model may evolve the capacity to detect and follow an environmental cue that indicates a preferred migration route. The strategy space is defined by the level of investment in acquiring personal information about this route or the alternative tendency to follow the direction choice of others. The result is a relation between the migratory process and a game theoretic dynamic that is generally applicable to situations where information may be considered a public good. Through the use of an approximation of social interactions, we demonstrate the emergence of a stable, polymorphic population consisting of an uninformed subpopulation that is dependent upon a specialized group of leaders. The branching process is classified using the techniques of adaptive dynamics.

Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation.

Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomena observed in these systems in a variety of domains, formally fitting these models to data remains intractable. Recently, approximate Bayesian computation (ABC) has been shown to be an effective approach to inference if the likelihood function for a model is unavailable. However, a key difficulty in successfully implementing ABC lies with the design, selection and weighting of appropriate summary statistics, a challenge that is especially acute when modelling high dimensional complex systems. In this work, we combine a Gaussian process accelerated ABC method with the automatic learning of summary statistics via graph neural networks. Our approach bypasses the need to design a model-specific set of summary statistics for inference. Instead, we encode relational inductive biases into a neural network using a graph embedding and then extract summary statistics automatically from simulation data. To evaluate our framework, we use a model of collective animal movement as a test bed and compare our method to a standard summary statistics approach and a linear regression-based algorithm.

Signalling and the evolution of cooperative foraging in dynamic environments.

Understanding cooperation in animal social groups remains a significant challenge for evolutionary theory. Observed behaviours that benefit others but incur some cost appear incompatible with classical notions of natural selection; however, these behaviours may be explained by concepts such as inclusive fitness, reciprocity, intra-specific mutualism or manipulation. In this work, we examine a seemingly altruistic behaviour, the active recruitment of conspecifics to a food resource through signalling. Here collective, cooperative behaviour may provide highly nonlinear benefits to individuals, since group functionality has the potential to be far greater than the sum of the component parts, for example by enabling the effective tracking of a dynamic resource. We show that due to this effect, signalling to others is an evolutionarily stable strategy under certain environmental conditions, even when there is a cost associated to this behaviour. While exploitation is possible, in the limiting case of a sparse, ephemeral but locally abundant nutrient source, a given environmental profile will support a fixed number of signalling individuals. Through a quantitative analysis, this effective carrying capacity for cooperation is related to the characteristic length and time scales of the resource field.

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.

Social information use and collective foraging in a pursuit diving seabird.

Individuals of many species utilise social information whilst making decisions. While many studies have examined social information in making large scale decisions, there is increasing interest in the use of fine scale social cues in groups. By examining the use of these cues and how they alter behaviour, we can gain insights into the adaptive value of group behaviours. We investigated the role of social information in choosing when and where to dive in groups of socially foraging European shags. From this we aimed to determine the importance of social information in the formation of these groups. We extracted individuals' surface trajectories and dive locations from video footage of collective foraging and used computational Bayesian methods to infer how social interactions influence diving. Examination of group spatial structure shows birds form structured aggregations with higher densities of conspecifics directly in front of and behind focal individuals. Analysis of diving behaviour reveals two distinct rates of diving, with birds over twice as likely to dive if a conspecific dived within their visual field in the immediate past. These results suggest that shag group foraging behaviour allows individuals to sense and respond to their environment more effectively by making use of social cues.

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'.

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

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity.This article is part of the theme issue 'Collective movement ecology'.

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'.

High-predation habitats affect the social dynamics of collective exploration in a shoaling fish.

Collective decisions play a major role in the benefits that animals gain from living in groups. Although the mechanisms of how groups collectively make decisions have been extensively researched, the response of within-group dynamics to ecological conditions is virtually unknown, despite adaptation to the environment being a cornerstone in biology. We investigate how within-group interactions during exploration of a novel environment are shaped by predation, a major influence on the behavior of prey species. We tested guppies (Poecilia reticulata) from rivers varying in predation risk under controlled laboratory conditions and find the first evidence of differences in group interactions between animals adapted to different levels of predation. Fish from high-predation habitats showed the strongest negative relationship between initiating movements and following others, which resulted in less variability in the total number of movements made between individuals. This relationship between initiating movements and following others was associated with differentiation into initiators and followers, which was only observed in fish from high-predation rivers. The differentiation occurred rapidly, as trials lasted 5 min, and was related to shoal cohesion, where more diverse groups from high-predation habitats were more cohesive. Our results show that even within a single species over a small geographical range, decision-making in a social context can vary with local ecological factors.

Collective behavior as a driver of critical transitions in migratory populations.

BACKGROUND: Mass migrations are among the most striking examples of animal movement in the natural world. Such migrations are major drivers of ecosystem processes and strongly influence the survival and fecundity of individuals. For migratory animals, a formidable challenge is to find their way over long distances and through complex, dynamic environments. However, recent theoretical and empirical work suggests that by traveling in groups, individuals are able to overcome these challenges and increase their ability to navigate. Here we use models to explore the implications of collective navigation on migratory, and population, dynamics, for both breeding migrations (to-and-fro migrations between distinct, fixed, end-points) and feeding migrations (loop migrations that track favorable conditions). RESULTS: We show that while collective navigation does improve a population's ability to migrate accurately, it can lead to Allee effects, causing the sudden collapse of populations if numbers fall below a critical threshold. In some scenarios, hysteresis prevents the migration from recovering even after the cause of the collapse has been removed. In collectively navigating populations that are locally adapted to specific breeding sites, a slight increase in mortality can cause a collapse of genetic population structure, rather than population size, making it more difficult to detect and prevent. CONCLUSIONS: Despite the large interest in collective behavior and its ubiquity in many migratory species, there is a notable lack of studies considering the implications of social navigation on the ecological dynamics of migratory species. Here we highlight the potential for a previously overlooked Allee effect in socially migrating species that may be important for conservation and management of such species.

Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts.

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.

On the evolutionary interplay between dispersal and local adaptation in heterogeneous environments.

Dispersal, whether in the form of a dandelion seed drifting on the breeze, or a salmon migrating upstream to breed in a nonnatal stream, transports genes between locations. At these locations, local adaptation modifies the gene frequencies so their carriers are better suited to particular conditions, be those of newly disturbed soil or a quiet river pool. Both dispersal and local adaptation are major drivers of population structure; however, in general, their respective roles are not independent and the two may often be at odds with one another evolutionarily, each one exhibiting negative feedback on the evolution of the other. Here, we investigate their joint evolution within a simple, discrete-time, metapopulation model. Depending on environmental conditions, their evolutionary interplay leads to either a monomorphic population of highly dispersing generalists or a collection of rarely dispersing, locally adapted, polymorphic sub-populations, each adapted to a particular habitat type. A critical value of environmental heterogeneity divides these two selection regimes and the nature of the transition between them is determined by the level of kin competition. When kin competition is low, at the transition we observe discontinuities, bistability, and hysteresis in the evolved strategies; however, when high, kin competition moderates the evolutionary feedback and the transition is smooth.

Social information use and the evolution of unresponsiveness in collective systems.

Animal groups in nature often display an enhanced collective information-processing capacity. It has been speculated that natural selection will tune this response to be optimal, ensuring that the group is reactive while also being robust to noise. Here, we show that this is unlikely to be the case. By using a simple model of decision-making in a dynamic environment, we find that when individuals behave rationally and are subject to selection based on their accuracy, optimality of collective decision-making is not attained. Instead, individuals overly rely on social information and evolve to be too readily influenced by their neighbours. This is due to a classic evolutionary conflict between individual and collective interest. The result is a sub-optimal system that is poised on the cusp of total unresponsiveness. Individuals in the evolved group exhibit delayed reactions to changes in the environment, before responding with rapid, socially reinforced transitions, reminiscent of familiar human and animal social systems (markets, stampedes, fashions, etc.). Our results demonstrate that behaviour of this type may not be pathological, but instead could represent an evolutionary attractor for such collective systems.

The evolution of distributed sensing and collective computation in animal populations.

Many animal groups exhibit rapid, coordinated collective motion. Yet, the evolutionary forces that cause such collective responses to evolve are poorly understood. Here, we develop analytical methods and evolutionary simulations based on experimental data from schooling fish. We use these methods to investigate how populations evolve within unpredictable, time-varying resource environments. We show that populations evolve toward a distinctive regime in behavioral phenotype space, where small responses of individuals to local environmental cues cause spontaneous changes in the collective state of groups. These changes resemble phase transitions in physical systems. Through these transitions, individuals evolve the emergent capacity to sense and respond to resource gradients (i.e. individuals perceive gradients via social interactions, rather than sensing gradients directly), and to allocate themselves among distinct, distant resource patches. Our results yield new insight into how natural selection, acting on selfish individuals, results in the highly effective collective responses evident in nature.

Emergent sensing of complex environments by mobile animal groups.

The capacity for groups to exhibit collective intelligence is an often-cited advantage of group living. Previous studies have shown that social organisms frequently benefit from pooling imperfect individual estimates. However, in principle, collective intelligence may also emerge from interactions between individuals, rather than from the enhancement of personal estimates. Here, we reveal that this emergent problem solving is the predominant mechanism by which a mobile animal group responds to complex environmental gradients. Robust collective sensing arises at the group level from individuals modulating their speed in response to local, scalar, measurements of light and through social interaction with others. This distributed sensing requires only rudimentary cognition and thus could be widespread across biological taxa, in addition to being appropriate and cost-effective for robotic agents.

Visual sensory networks and effective information transfer in animal groups.

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a 'shell' of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals' detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups.

Uninformed individuals promote democratic consensus in animal groups.

Conflicting interests among group members are common when making collective decisions, yet failure to achieve consensus can be costly. Under these circumstances individuals may be susceptible to manipulation by a strongly opinionated, or extremist, minority. It has previously been argued, for humans and animals, that social groups containing individuals who are uninformed, or exhibit weak preferences, are particularly vulnerable to such manipulative agents. Here, we use theory and experiment to demonstrate that, for a wide range of conditions, a strongly opinionated minority can dictate group choice, but the presence of uninformed individuals spontaneously inhibits this process, returning control to the numerical majority. Our results emphasize the role of uninformed individuals in achieving democratic consensus amid internal group conflict and informational constraints.

The social context of cannibalism in migratory bands of the Mormon cricket.

Cannibalism has been shown to be important to the collective motion of mass migratory bands of insects, such as locusts and Mormon crickets. These mobile groups consist of millions of individuals and are highly destructive to vegetation. Individuals move in response to attacks from approaching conspecifics and bite those ahead, resulting in further movement and encounters with others. Despite the importance of cannibalism, the way in which individuals make attack decisions and how the social context affects these cannibalistic interactions is unknown. This can be understood by examining the decisions made by individuals in response to others. We performed a field investigation which shows that adult Mormon crickets were more likely to approach and attack a stationary cricket that was side-on to the flow than either head- or abdomen-on, suggesting that individuals could reduce their risk of an attack by aligning with neighbours. We found strong social effects on cannibalistic behaviour: encounters lasted longer, were more likely to result in an attack, and attacks were more likely to be successful if other individuals were present around a stationary individual. This local aggregation appears to be driven by positive feedback whereby the presence of individuals attracts others, which can lead to further crowding. This work improves our understanding of the local social dynamics driving migratory band formation, maintenance and movement at the population level.