The importance of individual variation in the dynamics of animal collective movements

Agents seeking long-term access to the wisdom of the crowd reduce immediate decision-making accuracy

Living in groups offers social animals the significant advantage of accessing collective wisdom and enhanced information processing, enabling more accurate decisions related to foraging, navigation and habitat selection. Preserving group membership is crucial for sustaining access to collective wisdom, incentivizing animals to prioritize group cohesion. However, when individuals encounter divergent information about the quality of various options, this can create a conflict between pursuing immediate rewards and the maintenance of group membership to improve access to future pay-offs. In this study, I show that rational agents who seek to maximize long-term rewards will be more inclined to follow the decisions of their peers than those with short-term horizons. In doing so, they necessarily make less-rewarding decisions in the short-term, which manifests in a lower individual accuracy when choosing the better of two options. Furthermore, I demonstrate that intuitions about collective wisdom can be misleading in groups of agents who prioritize long-term rewards, with disagreement being a better signal for the accuracy of collective choices than consensus. These results demonstrate that observed patterns of sociality should be interpreted in the context of the life history of an individual and its peers, rather than through the lens of an isolated decision.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.

Dynamic Leadership Mechanism in Homing Pigeon Flocks

In recent years, an increasing number of studies have focused on exploring the principles and mechanisms underlying the emergence of collective intelligence in biological populations, aiming to provide insights for human society and the engineering field. Pigeon flock behavior garners significant attention as a subject of study. Collective homing flight is a commonly observed behavioral pattern in pigeon flocks. The study analyzes GPS data during the homing process and utilizes acceleration information, which better reflects the flock's movement tendencies during turns, to describe the leadership relationships within the group. By examining the evolution of acceleration during turning, the study unveils a dynamic leadership mechanism before and after turns, employing a more intricate dynamic model to depict the flock's motion. Specifically, during stable flight, pigeon flocks tend to rely on fixed leaders to guide homing flight, whereas during turns, individuals positioned in the direction of the flock's turn experience a notable increase in their leadership status. These findings suggest the existence of a dynamic leadership mechanism within pigeon flocks, enabling adaptability and stability under diverse flight conditions. From an engineering perspective, this leadership mechanism may offer novel insights for coordinating industrial multi-robot systems and controlling drone formations.

Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reduction

Collective motion of locally interacting agents is found ubiquitously throughout nature. The inability to probe individuals has driven longstanding interest in the development of methods for inferring the underlying interactions. In the context of heterogeneous collectives, where the population consists of individuals driven by different interactions, existing approaches require some knowledge about the heterogeneities or underlying interactions. Here, we investigate the feasibility of identifying the identities in a heterogeneous collective without such prior knowledge. We numerically explore the behavior of a heterogeneous Vicsek model and find sufficiently long trajectories intrinsically cluster in a principal component analysis-based dimensionally reduced model-agnostic description of the data. We identify how heterogeneities in each parameter in the model (interaction radius, noise, population proportions) dictate this clustering. Finally, we show the generality of this phenomenon by finding similar behavior in a heterogeneous D'Orsogna model. Altogether, our results establish and quantify the intrinsic model-agnostic statistical disentanglement of identities in heterogeneous collectives.

Spatial distribution of heterogeneity as a modulator of collective dynamics in pancreatic beta-cell networks and beyond

We study the impact of spatial distribution of heterogeneity on collective dynamics in gap-junction coupled beta-cell networks comprised on cells from two populations that differ in their intrinsic excitability. Initially, these populations are uniformly and randomly distributed throughout the networks. We develop and apply an iterative algorithm for perturbing the arrangement of the network such that cells from the same population are increasingly likely to be adjacent to one another. We find that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity decreases as network sortedness increases. Moreover, for weak coupling, we find that regimes of partial synchronisation and wave propagation arise, which depend both on network drive and network sortedness. We then demonstrate the utility of this algorithm for studying the distribution of heterogeneity in general networks, for which we use Watts-Strogatz networks as a case study. This work highlights the importance of heterogeneity in node dynamics in establishing collective rhythms in complex, excitable networks and has implications for a wide range of real-world systems that exhibit such heterogeneity.

Categorizing the geometry of animal diel movement patterns with examples from high-resolution barn owl tracking

BACKGROUND

Movement is central to understanding the ecology of animals. The most robustly definable segments of an individual's lifetime track are its diel activity routines (DARs). This robustness is due to fixed start and end points set by a 24-h clock that depends on the individual's quotidian schedule. An analysis of day-to-day variation in the DARs of individuals, their comparisons among individuals, and the questions that can be asked, particularly in the context of lunar and annual cycles, depends on the relocation frequency and spatial accuracy of movement data. Here we present methods for categorizing the geometry of DARs for high frequency (seconds to minutes) movement data.

METHODS

Our method involves an initial categorization of DARs using data pooled across all individuals. We approached this categorization using a Ward clustering algorithm that employs four scalar "whole-path metrics" of trajectory geometry: 1. net displacement (distance between start and end points), 2. maximum displacement from start point, 3. maximum diameter, and 4. maximum width. We illustrate the general approach using reverse-GPS data obtained from 44 barn owls, Tyto alba, in north-eastern Israel. We conducted a principle components analysis (PCA) to obtain a factor, PC1, that essentially captures the scale of movement. We then used a generalized linear mixed model with PC1 as the dependent variable to assess the effects of age and sex on movement.

RESULTS

We clustered 6230 individual DARs into 7 categories representing different shapes and scale of the owls nightly routines. Five categories based on size and elongation were classified as closed (i.e. returning to the same roost), one as partially open (returning to a nearby roost) and one as fully open (leaving for another region). Our PCA revealed that the DAR scale factor, PC1, accounted for 86.5% of the existing variation. It also showed that PC2 captures the openness of the DAR and accounted for another 8.4% of the variation. We also constructed spatio-temporal distributions of DAR types for individuals and groups of individuals aggregated by age, sex, and seasonal quadrimester, as well as identify some idiosyncratic behavior of individuals within family groups in relation to location. Finally, we showed in two ways that DARs were significantly larger in young than adults and in males than females.

CONCLUSION

Our study offers a new method for using high-frequency movement data to classify animal diel movement routines. Insights into the types and distributions of the geometric shape and size of DARs in populations may well prove to be more invaluable for predicting the space-use response of individuals and populations to climate and land-use changes than other currently used movement track methods of analysis.

Behavioural ecology at the spatial-social interface

Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the 'spatial-social interface' as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial-social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial-social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial-social interface.

A fellow-following-principle based group model and its application to fish school analysis

Group models based on simple rules are viewed as a bridge to clarifying animal group movements. The more similar a model to real-world observations, the closer it is to the essence of such movements. Inspired by the fish school, this study suggests a principle called fellow-following for group movements. More specifically, a simple-rules-based model was proposed and extended into a set of concrete rules, and two- and three-dimensional group models were established. The model results are intuitively similar to the fish school, and when the group size increases, the milling phase of both the model and fish school tends from unstable to stable. Further, we proposed a novel order parameter and a similarity measurement framework for group structures. The proposed model indicates the intuition similarity, consistency of dynamic characteristics, and static structure similarity with fish schools, which suggests that the principle of fellow-following may reveal the essence of fish school movements. Our work suggests a different approach for the self-organized formation of a swarm robotic system based on local information.

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.

A Data-Driven Simulation of the Trophallactic Network and Intranidal Food Flow Dissemination in Ants

Food sharing can occur in both social and non-social species, but it is crucial in eusocial species, in which only some group members collect food. This food collection and the intranidal (i.e., inside the nest) food distribution through trophallactic (i.e., mouth-to-mouth) exchanges are fundamental in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchange are poorly understood. In this study, we provide new insights into the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and the division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective levels. We also test the alternative hypotheses (no variability and no division of labour). The division of labour, combined with inter-individual variability, leads to predictions of the food dynamics and exchange networks that run, contrary to the other models. Our results suggest a link between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and the network of trophallactic events. Our results show that a slight level of heterogeneity in the number of trophallactic events is enough to generate the properties of the experimental networks and seems to be crucial for the creation of efficient trophallactic networks. Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the networks observed in ants, leading to a better understanding of the evolution of self-organisation in such societies.

Cascading indirect genetic effects in a clonal vertebrate

Understanding how individual differences arise and how their effects propagate through groups are fundamental issues in biology. Individual differences can arise from indirect genetic effects (IGE): genetically based variation in the conspecifics with which an individual interacts. Using a clonal species, the Amazon molly (Poecilia formosa), we test the hypothesis that IGE can propagate to influence phenotypes of the individuals that do not experience them firsthand. We tested this by exposing genetically identical Amazon mollies to conspecific social partners of different clonal lineages, and then moving these focal individuals to new social groups in which they were the only member to have experienced the IGE. We found that genetically different social environments resulted in the focal animals experiencing different levels of aggression, and that these IGE carried over into new social groups to influence the behaviour of naive individuals. These data reveal that IGE can cascade beyond the individuals that experience them. Opportunity for cascading IGE is ubiquitous, especially in species with long-distance dispersal or fission-fusion group dynamics. Cascades could amplify (or mitigate) the effects of IGE on trait variation and on evolutionary trajectories. Expansion of the IGE framework to include cascading and other types of carry-over effects will therefore improve understanding of individual variation and social evolution and allow more accurate prediction of population response to changing environments.

A Strategy Adaptive Evolution Approach Based on the Public Goods Game

Cluster behavior is prevalent in nature. Many individuals change their behavior to adapt to a dynamically changing environment by following simple rules of behavior and interacting with information from neighboring individuals. In this study, the traditional public goods game model is improved by combining the advantages of game theory and interactive learning. A strategy adaptive evolution method based on a public goods game is proposed. The emergence of cooperative behavior in weighted networks under the co-evolution of game strategies and node weights is explored in conjunction with multi-agent interactive learning. The results show that in a public goods game with strategic adaptation, a person’s influence becomes greater if their level of adaptation exceeds the desired level, and less otherwise. This weight adjustment is defined by the intensity parameter δ. A moderate δ value can effectively facilitate the occurrence of cooperative evolution. The level of cooperation depends mainly on the weight distribution of participants, which leads to the formation of cooperative clusters controlled by high-weighted cooperators. Even with the great temptation to defect, these cooperators can prevail over defectors. The adjustment of node weights increases the heterogeneity of individuals. This research provides a viable pathway to solve social dilemmas and will further promote the application of multi-agent intelligent decision making.

Life in 2.5D: Animal Movement in the Trees

The complex, interconnected, and non-contiguous nature of canopy environments present unique cognitive, locomotor, and sensory challenges to their animal inhabitants. Animal movement through forest canopies is constrained; unlike most aquatic or aerial habitats, the three-dimensional space of a forest canopy is not fully realized or available to the animals within it. Determining how the unique constraints of arboreal habitats shape the ecology and evolution of canopy-dwelling animals is key to fully understanding forest ecosystems. With emerging technologies, there is now the opportunity to quantify and map tree connectivity, and to embed the fine-scale horizontal and vertical position of moving animals into these networks of branching pathways. Integrating detailed multi-dimensional habitat structure and animal movement data will enable us to see the world from the perspective of an arboreal animal. This synthesis will shed light on fundamental aspects of arboreal animals’ cognition and ecology, including how they navigate landscapes of risk and reward and weigh energetic trade-offs, as well as how their environment shapes their spatial cognition and their social dynamics.

Disentangling intrinsic motion from neighborhood effects in heterogeneous collective motion

Most real-world collectives, including animal groups, pedestrian crowds, active particles, and living cells, are heterogeneous. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals based on their observed movement patterns. However, the true individual properties may be masked by the nonlinear interactions in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects, such as jamming and clustering, affect individual motion, the information in an agent's own movement is insufficient for accurate classification. A simple observer algorithm, based only on individual velocities, cannot accurately estimate the level of heterogeneity of the system and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent's motion are explicitly accounted for. We use insights about the phenomenology of collective motion to quantify the effect of the neighborhood on an agent's motion using a neighborhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighborhood effects is often necessary to correctly infer intrinsic properties of individuals.

Studies of the Behavioral Sequences: The Neuroethological Morphology Concept Crossing Ethology and Functional Morphology

Simple Summary

Behavioral sequences analysis is a relevant method for quantifying the behavioral repertoire of animals to respond to the classical Tinbergen’s four questions. Research in ethology and functional morphology intercepts at the level of analysis of behaviors through the recording and interpretation of data from of movement sequence studies with various types of imaging and sensor systems. We propose the concept of Neuroethological morphology to build a holistic framework for understanding animal behavior. This concept integrates ethology (including behavioral ecology and neuroethology) with functional morphology (including biomechanics and physics) to provide a heuristic approach in behavioral biology.

Abstract

Postures and movements have been one of the major modes of human expression for understanding and depicting organisms in their environment. In ethology, behavioral sequence analysis is a relevant method to describe animal behavior and to answer Tinbergen’s four questions testing the causes of development, mechanism, adaptation, and evolution of behaviors. In functional morphology (and in biomechanics), the analysis of behavioral sequences establishes the motor pattern and opens the discussion on the links between “form” and “function”. We propose here the concept of neuroethological morphology in order to build a holistic framework for understanding animal behavior. This concept integrates ethology with functional morphology, and physics. Over the past hundred years, parallel developments in both disciplines have been rooted in the study of the sequential organization of animal behavior. This concept allows for testing genetic, epigenetic, and evo-devo predictions of phenotypic traits between structures, performances, behavior, and fitness in response to environmental constraints. Based on a review of the literature, we illustrate this concept with two behavioral cases: (i) capture behavior in squamates, and (ii) the ritualistic throat display in lizards.

Diverse perspectives from diverse scholars are vital for theoretical biology

Science is based on studying some aspects of the world while holding others constant. The assumptions of what can and cannot be ignored implicitly shape our understanding of the world around us. This truth is particularly evident when studying biology through mathematical models, where one must explicitly state assumptions during the process of model building. Although we often recognize that all models are “wrong” in their assumptions, we often overlook the corollary that developing multiple models that are wrong in different ways can help us triangulate truth in our understanding. Theoretical biologists build models in the image of how they envision the world, an image that is shaped by their scientific identity, experiences, and perspectives. A lack of diversity in any of these axes handicaps our ability to understand biological systems through theory. However, we can overcome this by collectively recognizing our own assumptions, by understanding how perspective shapes the development of theory, and — most importantly — by increasing the diversity of theoretical biologists (in terms of identity, experiences, and perspectives). Combined, this will lead to developing theory that provides a richer understanding of the biological world.

Leapfrog migration and residents: New migratory habits in Swedish Greylag geese

Knowledge about intraspecific and individual variation in bird migration behavior is important to predict spatiotemporal distribution, patterns of phenology, breeding success, and interactions with the surrounding environment (e.g., human livelihoods). Such variation is key to adaptive, evolutionary responses, i.e., how individuals respond spatiotemporally to the environment to maximize fitness. In this study we used GPS location data from one to three full annual cycles from 76 Greylag geese (Anser anser) to test the hypothesis that geese originating at five latitudinally separated capture sites in Sweden have different migration strategies. We also assessed individual consistency in movement strategy over consecutive annual cycles. We used the scale-independent net squared displacement modeling framework to quantify variables of autumn and spring migration for geese from each capture site: distance, timing, and duration. Our results demonstrate a positive correlation between migration distance and latitudinal origin. Geese from the northernmost site on average migrated farther south and about 15 times as far as the short-moving or resident geese from the two southernmost sites. Movement strategies of individual geese varied considerably both within and among capture sites. Individual consistency in movement strategy from one annual cycle to the consecutive was high in geese from the northern sites moving the farthest, whereas the resident or short-moving geese from the southernmost sites generally showed lower or no individual consistency. These changes have come about during a time span so short (i.e., ca. 35 years or 8-10 generations) that it can unlikely be explained by classical Darwinian between-generation adaptation. Consequently, and given that young geese follow their parents during their first migration, we presume an important role of within-family, inter-generation change as a driver behind the large-scale changed migration habits in Swedish Greylag geese.

Emergence of splits and collective turns in pigeon flocks under predation

Complex patterns of collective behaviour may emerge through self-organization, from local interactions among individuals in a group. To understand what behavioural rules underlie these patterns, computational models are often necessary. These rules have not yet been systematically studied for bird flocks under predation. Here, we study airborne flocks of homing pigeons attacked by a robotic falcon, combining empirical data with a species-specific computational model of collective escape. By analysing GPS trajectories of flocking individuals, we identify two new patterns of collective escape: early splits and collective turns, occurring even at large distances from the predator. To examine their formation, we extend an agent-based model of pigeons with a 'discrete' escape manoeuvre by a single initiator, namely a sudden turn interrupting the continuous coordinated motion of the group. Both splits and collective turns emerge from this rule. Their relative frequency depends on the angular velocity and position of the initiator in the flock: sharp turns by individuals at the periphery lead to more splits than collective turns. We confirm this association in the empirical data. Our study highlights the importance of discrete and uncoordinated manoeuvres in the collective escape of bird flocks and advocates the systematic study of their patterns across species.

The Potential for Physiological Performance Curves to Shape Environmental Effects on Social Behavior

As individual animals are exposed to varying environmental conditions, phenotypic plasticity will occur in a vast array of physiological traits. For example, shifts in factors such as temperature and oxygen availability can affect the energy demand, cardiovascular system, and neuromuscular function of animals that in turn impact individual behavior. Here, we argue that nonlinear changes in the physiological traits and performance of animals across environmental gradients—known as physiological performance curves—may have wide-ranging effects on the behavior of individual social group members and the functioning of animal social groups as a whole. Previous work has demonstrated how variation between individuals can have profound implications for socially living animals, as well as how environmental conditions affect social behavior. However, the importance of variation between individuals in how they respond to changing environmental conditions has so far been largely overlooked in the context of animal social behavior. First, we consider the broad effects that individual variation in performance curves may have on the behavior of socially living animals, including: (1) changes in the rank order of performance capacity among group mates across environments; (2) environment-dependent changes in the amount of among- and within-individual variation, and (3) differences among group members in terms of the environmental optima, the critical environmental limits, and the peak capacity and breadth of performance. We then consider the ecological implications of these effects for a range of socially mediated phenomena, including within-group conflict, within- and among group assortment, collective movement, social foraging, predator-prey interactions and disease and parasite transfer. We end by outlining the type of empirical work required to test the implications for physiological performance curves in social behavior.

Context is Key: Social Environment Mediates the Impacts of a Psychoactive Pollutant on Shoaling Behavior in Fish

Behavior-modifying drugs, such as antidepressants, are increasingly being detected in waterways and aquatic wildlife around the globe. Typically, behavioral effects of these contaminants are assessed using animals tested in social isolation. However, for group-living species, effects seen in isolation may not reflect those occurring in realistic social settings. Furthermore, interactions between chemical pollution and other stressors, such as predation risk, are seldom considered. This is true even though animals in the wild are rarely, if ever, confronted by chemical pollution as a single stressor. Here, in a 2 year multigenerational experiment, we tested for effects of the antidepressant fluoxetine (measured concentrations [±SD]: 42.27 ± 36.14 and 359.06 ± 262.65 ng/L) on shoaling behavior in guppies (Poecilia reticulata) across different social contexts and under varying levels of perceived predation risk. Shoaling propensity and shoal choice (choice of groups with different densities) were assessed in a Y-maze under the presence of a predatory or nonpredatory heterospecific, with guppies tested individually and in male-female pairs. When tested individually, no effect of fluoxetine was seen on shoaling behavior. However, in paired trials, high-fluoxetine-exposed fish exhibited a significantly greater shoaling propensity. Hence, effects of fluoxetine were mediated by social context, highlighting the importance of this fundamental but rarely considered factor when evaluating impacts of environmental pollution.

Locomotor compromises maintain group cohesion in baboon troops on the move

When members of a group differ in locomotor capacity, coordinating collective movement poses a challenge: some individuals may have to move faster (or slower) than their preferred speed to remain together. Such compromises have energetic repercussions, yet research in collective behaviour has largely neglected locomotor consensus costs. Here, we integrate high-resolution tracking of wild baboon locomotion and movement with simulations to demonstrate that size-based variation in locomotor capacity poses an obstacle to the collective movement. While all baboons modulate their gait and move-pause dynamics during collective movement, the costs of maintaining cohesion are disproportionately borne by smaller group members. Although consensus costs are not distributed equally, all group-mates do make locomotor compromises, suggesting a shared decision-making process drives the pace of collective movement in this highly despotic species. These results highlight the importance of considering how social dynamics and locomotor capacity interact to shape the movement ecology of group-living species.

Spatio-temporal variation in the wintering associations of an alpine bird

Many animals make behavioural changes to cope with winter conditions, being gregariousness a common strategy. Several factors have been invoked to explain why gregariousness may evolve during winter, with individuals coming together and separating as they trade off the different costs and benefits of living in groups. These trade-offs may, however, change over space and time as a response to varying environmental conditions. Despite its importance, little is known about the factors triggering gregarious behaviour during winter and its change in response to variation in weather conditions is poorly documented. Here, we aimed at quantifying large-scale patterns in wintering associations over 23 years of the white-winged snowfinch Montifringilla nivalis nivalis. We found that individuals gather in larger groups at sites with harsh wintering conditions. Individuals at colder sites reunite later and separate earlier in the season than at warmer sites. However, the magnitude and phenology of wintering associations are ruled by changes in weather conditions. When the temperature increased or the levels of precipitation decreased, group size substantially decreased, and individuals stayed united in groups for a shorter time. These results shed light on factors driving gregariousness and points to shifting winter climate as an important factor influencing this behaviour.

Collective motion as a distinct behavioral state of the individual

The collective motion of swarms depends on adaptations at the individual level. We explored these and their effects on swarm formation and maintenance in locusts. The walking kinematics of individual insects were monitored under laboratory settings, before, as well as during collective motion in a group, and again after separation from the group. It was found that taking part in collective motion induced in the individual unique behavioral kinematics, suggesting the existence of a distinct behavioral mode that we term a "collective-motion-state." This state, characterized by behavioral adaptation to the social context, is long lasting, not induced by crowding per se, but only by experiencing collective motion. Utilizing computational models, we show that this adaptability increases the robustness of the swarm. Overall, our findings suggest that collective motion is not only an emergent property of the group but also depends on a behavioral mode, rooted in endogenous mechanisms of the individual.

Social rank and not physiological capacity determines competitive success in zebrafish (Danio rerio)

Competition for resources shapes ecological and evolutionary relationships. Physiological capacities such as in locomotor performance can influence the fitness of individuals by increasing competitive success. Social hierarchy too can affect outcomes of competition by altering locomotor behaviour or because higher ranking individuals monopolize resources. Here, we tested the hypotheses that competitive success is determined by sprint performance or by social status. We show that sprint performance of individuals measured during escape responses (fast start) or in an accelerated sprint test did not correlate with realized sprint speed while competing for food within a social group of five fish; fast start and accelerated sprint speed were higher than realized speed. Social status within the group was the best predictor of competitive success, followed by realized speed. Social hierarchies in zebrafish are established within 7 days of their first encounter, and interestingly, there was a positive correlation between social status and realized speed 1 and 4 days after fish were placed in a group, but not after 7 days. These data indicate that physiological performance decreases in importance as social relationships are established. Also, maximal physiological capacities were not important for competitive success, but swimming speed changed with social context.

Artificial selection for schooling behaviour and its effects on associative learning abilities

The evolution of collective behaviour has been proposed to have important effects on individual cognitive abilities. Yet, in what way they are related remains enigmatic. In this context, the 'distributed cognition' hypothesis suggests that reliance on other group members relaxes selection for individual cognitive abilities. Here, we tested how cognitive processes respond to evolutionary changes in collective motion using replicate lines of guppies (Poecilia reticulata) artificially selected for the degree of schooling behaviour (group polarization) with >15% difference in schooling propensity. We assessed associative learning in females of these selection lines in a series of cognitive assays: colour associative learning, reversal learning, social associative learning, and individual and collective spatial associative learning. We found that control females were faster than polarization-selected females at fulfilling a learning criterion only in the colour associative learning assay, but they were also less likely to reach a learning criterion in the individual spatial associative learning assay. Hence, although testing several cognitive domains, we found weak support for the distributed cognition hypothesis. We propose that any cognitive implications of selection for collective behaviour lie outside of the cognitive abilities included in food-motivated associative learning for visual and spatial cues.

Modelling group movement with behaviour switching in continuous time

This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi-domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between 'following' and 'independent'. The 'following' movement is modelled through a linear stochastic differential equation, while the 'independent' movement is modelled as Brownian motion. The movement of the leading point is modelled either as an Ornstein-Uhlenbeck (OU) process or as Brownian motion (BM), which makes the whole system a higher-dimensional Ornstein-Uhlenbeck process, possibly an intrinsic non-stationary version. An inhomogeneous Kalman filter Markov chain Monte Carlo algorithm is developed to estimate the diffusion and switching parameters and the behaviour states of each individual at a given time point. The method successfully recovers the true behavioural states in simulated data sets , and is also applied to model a group of simultaneously tracked reindeer (Rangifer tarandus).

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.

Individual learning phenotypes drive collective behavior

Individual differences in learning can influence how animals respond to and communicate about their environment, which may nonlinearly shape how a social group accomplishes a collective task. There are few empirical examples of how differences in collective dynamics emerge from variation among individuals in cognition. Here, we use a naturally variable and heritable learning behavior called latent inhibition (LI) to show that interactions among individuals that differ in this cognitive ability drive collective foraging behavior in honey bee colonies. We artificially selected two distinct phenotypes: high-LI bees that ignore previously familiar stimuli in favor of novel ones and low-LI bees that learn familiar and novel stimuli equally well. We then provided colonies differentially composed of different ratios of these phenotypes with a choice between familiar and novel feeders. Colonies of predominantly high-LI individuals preferred to visit familiar food locations, while low-LI colonies visited novel and familiar food locations equally. Interestingly, in colonies of mixed learning phenotypes, the low-LI individuals showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low-LI group. We show that the shift in feeder preference of low-LI bees is driven by foragers of the high-LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their social interactions, which we argue relate to differences in attention, drive emergent collective outcomes.

Causes and consequences of individual variation in animal movement

Animal movement comes in a variety of 'types' including small foraging movements, larger one-way dispersive movements, seasonally-predictable round-trip migratory movements, and erratic nomadic movements. Although most individuals move at some point throughout their lives, movement patterns can vary widely across individuals within the same species: differing within an individual over time (intra-individual), among individuals in the same population (inter-individual), or among populations (inter-population). Yet, studies of movement (theoretical and empirical alike) more often focus on understanding 'typical' movement patterns than understanding variation in movement. Here, I synthesize current knowledge of movement variation (drawing parallels across species and movement types), describing the causes (what factors contribute to individual variation), patterns (what movement variation looks like), consequences (why variation matters), maintenance (why variation persists), implications (for management and conservation), and finally gaps (what pieces we are currently missing). By synthesizing across scales of variation, I span across work on plasticity, personality, and geographic variation. Individual movement can be driven by factors that act at the individual, population, community and ecosystem level and have ramifications at each of these levels. Generally the consequences of movement are less well understood than the causes, in part because the effects of movement variation are often nested, with variation manifesting at the population level, which in turn affects communities and ecosystems. Understanding both cause and consequence is particularly important for predicting when variation begets variation in a positive feedback loop, versus when a negative feedback causes variation to be dampened successively. Finally, maintaining standing variation in movement may be important for facilitating species' ability to respond to future environmental change.

The physiology of movement

Movement, from foraging to migration, is known to be under the influence of the environment. The translation of environmental cues to individual movement decision making is determined by an individual's internal state and anticipated to balance costs and benefits. General body condition, metabolic and hormonal physiology mechanistically underpin this internal state. These physiological determinants are tightly, and often genetically linked with each other and hence central to a mechanistic understanding of movement. We here synthesise the available evidence of the physiological drivers and signatures of movement and review (1) how physiological state as measured in its most coarse way by body condition correlates with movement decisions during foraging, migration and dispersal, (2) how hormonal changes underlie changes in these movement strategies and (3) how these can be linked to molecular pathways. We reveale that a high body condition facilitates the efficiency of routine foraging, dispersal and migration. Dispersal decision making is, however, in some cases stimulated by a decreased individual condition. Many of the biotic and abiotic stressors that induce movement initiate a physiological cascade in vertebrates through the production of stress hormones. Movement is therefore associated with hormone levels in vertebrates but also insects, often in interaction with factors related to body or social condition. The underlying molecular and physiological mechanisms are currently studied in few model species, and show -in congruence with our insights on the role of body condition- a central role of energy metabolism during glycolysis, and the coupling with timing processes during migration. Molecular insights into the physiological basis of movement remain, however, highly refractory. We finalise this review with a critical reflection on the importance of these physiological feedbacks for a better mechanistic understanding of movement and its effects on ecological dynamics at all levels of biological organization.

Behavioural plasticity and the transition to order in jackdaw flocks

Collective behaviour is typically thought to arise from individuals following fixed interaction rules. The possibility that interaction rules may change under different circumstances has thus only rarely been investigated. Here we show that local interactions in flocks of wild jackdaws (Corvus monedula) vary drastically in different contexts, leading to distinct group-level properties. Jackdaws interact with a fixed number of neighbours (topological interactions) when traveling to roosts, but coordinate with neighbours based on spatial distance (metric interactions) during collective anti-predator mobbing events. Consequently, mobbing flocks exhibit a dramatic transition from disordered aggregations to ordered motion as group density increases, unlike transit flocks where order is independent of density. The relationship between group density and group order during this transition agrees well with a generic self-propelled particle model. Our results demonstrate plasticity in local interaction rules and have implications for both natural and artificial collective systems.

Modelling collective behaviour in different circumstances remains a challenge because of uncertainty related to interaction rule changes. Here, the authors report plasticity in local interaction rules in flocks of wild jackdaws with implications for both natural and artificial collective systems.

Field-realistic antidepressant exposure disrupts group foraging dynamics in mosquitofish

Psychoactive pollutants, such as antidepressants, are increasingly detected in the environment. Mounting evidence suggests that such pollutants can disrupt the behaviour of non-target species. Despite this, few studies have considered how the response of exposed organisms might be mediated by social context. To redress this, we investigated the impacts of two environmentally realistic concentrations of a pervasive antidepressant pollutant, fluoxetine, on foraging behaviour in fish (Gambusia holbrooki), tested individually or in a group. Fluoxetine did not alter behaviour of solitary fish. However, in a group setting, fluoxetine exposure disrupted the frequency of aggressive interactions and food consumption, with observed effects being contingent on both the mean weight of group members and the level of within-group variation in weight. Our results suggest that behavioural tests in social isolation may not accurately predict the environmental risk of chemical pollutants for group-living species and highlight the potential for social context to mediate the effects of psychoactive pollutants in exposed wildlife.

Collective turns in jackdaw flocks: kinematics and information transfer

The rapid, cohesive turns of bird flocks are one of the most vivid examples of collective behaviour in nature, and have attracted much research. Three-dimensional imaging techniques now allow us to characterize the kinematics of turning and their group-level consequences in precise detail. We measured the kinematics of flocks of wild jackdaws executing collective turns in two contexts: during transit to roosts and anti-predator mobbing. All flocks reduced their speed during turns, probably because of constraints on individual flight capability. Turn rates increased with the angle of the turn so that the time to complete turns remained constant. We also find that context may alter where turns are initiated in the flocks: for transit flocks in the absence of predators, initiators were located throughout the flocks, but for mobbing flocks with a fixed ground-based predator, they were always located at the front. Moreover, in some transit flocks, initiators were far apart from each other, potentially because of the existence of subgroups and variation in individual interaction ranges. Finally, we find that as the group size increased the information transfer speed initially increased, but rapidly saturated to a constant value. Our results highlight previously unrecognized complexity in turning kinematics and information transfer in social animals.

Milling and meandering: Flocking dynamics of stochastically interacting agents with a field of view

We introduce a stochastic agent-based model for the flocking dynamics of self-propelled particles that exhibit nonlinear velocity-alignment interactions with neighbors within their field of view. The stochasticity in the dynamics is spatially heterogeneous and arises implicitly from the nature of the interparticle interactions. We observe long-time spatial cohesion in the emergent flocking dynamics, despite the absence of attractive forces that explicitly depend on the relative positions of particles. The wide array of flocking patterns exhibited by this model are characterized by identifying spatially distinct clusters and computing their corresponding angular momenta.

Collective Motion as an Ultimate Effect in Crowded Selfish Herds

The selfish herd hypothesis explains how social prey can assemble cohesive groups for maximising individual fitness. However, previous models often abstracted away the physical manifestation of the focal animals such that the influence of getting stuck in a crowded herd on individual adaptation was less intensively investigated. Here, we propose an evolutionary model to simulate the adaptation of egoistic social prey to predation given that individual mobility is strictly restrained by the presence of other conspecifics. In our simulated evolutionary races, agents were set to either be confined by neighbours or move to empty cells on the lattice, and the behavioural traits of those less exposed were selected and inherited. Our analyses show that under this crowded environment, cohesive and steady herds were consistently replaced by morphing and moving aggregates via the attempt of border agents to share predation risk with the inner members. This kind of collective motion emerges purely from the competition among selfish individuals regardless of any group benefit. Our findings reveal that including the crowding effect with the selfish herd scenario permits additional diversity in the predicted outcomes and imply that a wider set of collective animal behaviours are explainable purely by individual-level selection.

Fission-fusion dynamics and group-size-dependent composition in heterogeneous populations

Many animal groups are heterogeneous and may even consist of individuals of different species, called mixed-species flocks. Mathematical and computational models of collective animal movement behavior, however, typically assume that groups and populations consist of identical individuals. In this paper, using the mathematical framework of the coagulation-fragmentation process, we develop and analyze a model of merge and split group dynamics, also called fission-fusion dynamics, for heterogeneous populations that contain two types (or species) of individuals. We assume that more heterogeneous groups experience higher split rates than homogeneous groups, forming two daughter groups whose compositions are drawn uniformly from all possible partitions. We analytically derive a master equation for group size and compositions and find mean-field steady-state solutions. We predict that there is a critical group size below which groups are more likely to be homogeneous and contain the abundant type or species. Despite the propensity of heterogeneous groups to split at higher rates, we find that groups are more likely to be heterogeneous but only above the critical group size. Monte Carlo simulation of the model show excellent agreement with these analytical model results. Thus, our model makes a testable prediction that composition of flocks are group-size-dependent and do not merely reflect the population level heterogeneity. We discuss the implications of our results to empirical studies on flocking systems.

Using activity and sociability to characterize collective motion

A wide range of measurements can be made on the collective motion of groups, and the movement of individuals within them. These include, but are not limited to: group size, polarization, speed, turning speed, speed or directional correlations, and distances to near neighbours. From an ecological and evolutionary perspective, we would like to know which of these measurements capture biologically meaningful aspects of an animal's behaviour and contribute to its survival chances. Previous simulation studies have emphasized two main factors shaping individuals' behaviour in groups; attraction and alignment. Alignment responses appear to be important in transferring information between group members and providing synergistic benefits to group members. Likewise, attraction to conspecifics is thought to provide benefits through, for example, selfish herding. Here, we use a factor analysis on a wide range of simple measurements to identify two main axes of collective motion in guppies (Poecilia reticulata): (i) sociability, which corresponds to attraction (and to a lesser degree alignment) to neighbours, and (ii) activity, which combines alignment with directed movement. We show that for guppies, predation in a natural environment produces higher degrees of sociability and (in females) lower degrees of activity, while female guppies sorted for higher degrees of collective alignment have higher degrees of both sociability and activity. We suggest that the activity and sociability axes provide a useful framework for measuring the behaviour of animals in groups, allowing the comparison of individual and collective behaviours within and between species.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’.

Migration in the Anthropocene: how collective navigation, environmental system and taxonomy shape the vulnerability of migratory species

Recent increases in human disturbance pose significant threats to migratory species using collective movement strategies. Key threats to migrants may differ depending on behavioural traits (e.g. collective navigation), taxonomy and the environmental system (i.e. freshwater, marine or terrestrial) associated with migration. We quantitatively assess how collective navigation, taxonomic membership and environmental system impact species' vulnerability by (i) evaluating population change in migratory and non-migratory bird, mammal and fish species using the Living Planet Database (LPD), (ii) analysing the role of collective navigation and environmental system on migrant extinction risk using International Union for Conservation of Nature (IUCN) classifications and (iii) compiling literature on geographical range change of migratory species. Likelihood of population decrease differed by taxonomic group: migratory birds were more likely to experience annual declines than non-migrants, while mammals displayed the opposite pattern. Within migratory species in IUCN, we observed that collective navigation and environmental system were important predictors of extinction risk for fishes and birds, but not for mammals, which had overall higher extinction risk than other taxa. We found high phylogenetic relatedness among collectively navigating species, which could have obscured its importance in determining extinction risk. Overall, outputs from these analyses can help guide strategic interventions to conserve the most vulnerable migrations.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'.

Intra- versus intergroup variance in collective behavior

Animal collective motion arises from the intricate interactions between the natural variability among individuals, and the homogenizing effect of the group, working to generate synchronization and maintain coherence. Here, these interactions were studied using marching locust nymphs under controlled laboratory settings. A novel experimental approach compared single animals, small groups, and virtual groups composed of randomly shuffled real members. We found that the locust groups developed unique, group-specific behavioral characteristics, reflected in large intergroup and small intragroup variance (compared with the shuffled groups). Behavioral features that differed between single animals and groups, but not between group types, were classified as essential for swarm formation. Comparison with Markov chain models showed that individual tendencies and the interaction network among animals dictate the group characteristics. Deciphering the bidirectional interactions between individual and group properties is essential for understanding the swarm phenomenon and predicting large-scale swarm behaviors.

Anatomy of leadership in collective behaviour

Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership," e.g., position, causality, influence, and information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group imply that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper, we develop an anatomy of leadership, identify several principal components, and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind, we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.

Friendship across species borders: factors that facilitate and constrain heterospecific sociality

Our understanding of animal sociality is based almost entirely on single-species sociality. Heterospecific sociality, although documented in numerous taxa and contexts, remains at the margins of sociality research and is rarely investigated in conjunction with single-species sociality. This could be because heterospecific and single-species sociality are thought to be based on fundamentally different mechanisms. However, our literature survey shows that heterospecific sociality based on mechanisms similar to single-species sociality is reported from many taxa, contexts and for various benefits. Therefore, we propose a conceptual framework to understand conspecific versus heterospecific social partner choice. Previous attempts, which are all in the context of social information, model partner choice as a trade-off between information benefit and competition cost, along a single phenotypic distance axis. Our framework of partner choice considers both direct grouping benefits and information benefits, allows heterospecific and conspecific partners to differ in degree and qualitatively, and uses a multi-dimensional trait space analysis of costs (competition and activity matching) and benefits (relevance of partner and quality of partner). We conclude that social partner choice is best-viewed as a continuum: some social benefits are obtainable only from conspecifics, some only from dissimilar heterospecifics, while many are potentially obtainable from conspecifics and heterospecifics.This article is part of the theme issue 'Collective movement ecology'.

Inferring influence and leadership in moving animal groups

Collective decision-making is a daily occurrence in the lives of many group-living animals, and can have critical consequences for the fitness of individuals. Understanding how decisions are reached, including who has influence and the mechanisms by which information and preferences are integrated, has posed a fundamental challenge. Here, we provide a methodological framework for studying influence and leadership in groups. We propose that individuals have influence if their actions result in some behavioural change among their group-mates, and are leaders if they consistently influence others. We highlight three components of influence (influence instances, total influence and consistency of influence), which can be assessed at two levels (individual-to-individual and individual-to-group). We then review different methods, ranging from individual positioning within groups to information-theoretic approaches, by which influence has been operationally defined in empirical studies, as well as how such observations can be aggregated to give insight into the underlying decision-making process. We focus on the domain of collective movement, with a particular emphasis on methods that have recently been, or are being, developed to take advantage of simultaneous tracking data. We aim to provide a resource bringing together methodological tools currently available for studying leadership in moving animal groups, as well as to discuss the limitations of current methodologies and suggest productive avenues for future research.This article is part of the theme issue 'Collective movement ecology'.