From local collective behavior to global migratory patterns in white storks

Searching for long timescales without fine tuning

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Long-distance communication can enable collective migration in a dynamic seascape

Social information is predicted to enhance the quality of animals' migratory decisions in dynamic ecosystems, but the relative benefits of social information in the long-range movements of marine megafauna are unknown. In particular, whether and how migrants use nonlocal information gained through social communication at the large spatial scale of oceanic ecosystems remains unclear. Here we test hypotheses about the cues underlying timing of blue whales' breeding migration in the Northeast Pacific via individual-based models parameterized by empirical behavioral data. Comparing emergent patterns from individual-based models to individual and population-level empirical metrics of migration timing, we find that individual whales likely rely on both personal and social sources of information about forage availability in deciding when to depart from their vast and dynamic foraging habitat and initiate breeding migration. Empirical patterns of migratory phenology can only be reproduced by models in which individuals use long-distance social information about conspecifics' behavioral state, which is known to be encoded in the patterning of their widely propagating songs. Further, social communication improves pre-migration seasonal foraging performance by over 60% relative to asocial movement mechanisms. Our results suggest that long-range communication enhances the perceptual ranges of migrating whales beyond that of any individual, resulting in increased foraging performance and more collective migration timing. These findings indicate the value of nonlocal social information in an oceanic migrant and suggest the importance of long-distance acoustic communication in the collective migration of wide-ranging marine megafauna.

Experience reduces route selection for conspecifics by the collectively migrating white stork

Migration can be an energetically costly behavior with strong fitness consequences in terms of mortality and reproduction.1,2,3,4,5,6,7,8,9,10,11 Migrants should select migratory routes to minimize their costs, but both costs and benefits may change with experience.12,13,14 This raises the question of whether experience changes how individuals select their migratory routes. Here, we investigate the effect of age on route selection criteria in a collectively migrating soaring bird, the white stork (Ciconia ciconia). We perform step-selection analysis on a longitudinal dataset tracking 158 white storks over up to 9 years to quantify how they select their routes based on the social and atmospheric environments and to examine how this selection changes with age. We find clear ontogenetic shifts in route selection criteria. Juveniles choose routes that have good atmospheric conditions and high conspecific densities. Yet, as they gain experience, storks' selection on the availability of social information reduces-after their fifth migration, experienced birds also choose routes with low conspecific densities. Thus, our results suggest that as individuals age, they gradually replace information gleaned from other individuals with information gained from experience, allowing them to shift their migration timing and increasing the timescale at which they select their routes.

Basak U, Mohiuddin M, Toda M, Komatsuzaki T. Inferring the roles of individuals in collective systems using information-theoretic measures of influence

In collective systems, influence of individuals can permeate an entire group through indirect interactionscom-plicating any scheme to understand individual roles from observations. A typical approach to understand an individuals influence on another involves consideration of confounding factors, for example, by conditioning on other individuals outside of the pair. This becomes unfeasible in many cases as the number of individuals increases. In this article, we review some of the unforeseen problems that arise in understanding individual influence in a collective such as single cells, as well as some of the recent works which address these issues using tools from information theory.

Learning shapes the development of migratory behavior

How animals refine migratory behavior over their lifetime (i.e., the ontogeny of migration) is an enduring question with important implications for predicting the adaptive capacity of migrants in a changing world. Yet, our inability to monitor the movements of individuals from early life onward has limited our understanding of the ontogeny of migration. The exploration-refinement hypothesis posits that learning shapes the ontogeny of migration in long-lived species, resulting in greater exploratory behavior early in life followed by more rapid and direct movement during later life. We test the exploration-refinement hypothesis by examining how white storks (Ciconia ciconia) balance energy, time, and information as they develop and refine migratory behavior during the first years of life. Here, we show that young birds reduce energy expenditure during flight while also increasing information gain by exploring new places during migration. As the birds age and gain more experience, older individuals stop exploring new places and instead move more quickly and directly, resulting in greater energy expenditure during migratory flight. During spring migration, individuals innovated novel shortcuts during the transition from early life into adulthood, suggesting a reliance on spatial memory acquired through learning. These incremental refinements in migratory behavior provide support for the importance of individual learning within a lifetime in the ontogeny of long-distance migration.

Synchrony of Bird Migration with Global Dispersal of Avian Influenza Reveals Exposed Bird Orders

Highly pathogenic avian influenza virus (HPAIV) A H5, particularly clade 2.3.4.4, has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, this mechanism has not been quantified using bird movement data from different species, and the timing and location of exposure of different species is unclear. We sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences of clade 2.3.4.4 and 2.3.2.1. First, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing exposed bird orders at geographical origins and destinations of HPAIV H5 clade 2.3.4.4 lineage movements, including relatively under-discussed orders. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behavior and life history in avian influenza studies.

Synchronous and Fully Steerable Active Particle Systems for Enhanced Mimicking of Collective Motion in Nature

The collective motion observed in living active matter, such as fish schools and bird flocks, is characterized by its dynamic and complex nature, involving various moving states and transitions. By tailoring physical interactions or incorporating information exchange capabilities, inanimate active particles can exhibit similar behavior. However, the lack of synchronous and arbitrary control over individual particles hinders their use as a test system for the study of more intricate collective motions in living species. Herein, a novel optical feedback control system that enables the mimicry of collective motion observed in living objects using active particles is proposed. This system allows for the experimental investigation of the velocity alignment, a seminal model of collective motion (known as the Vicsek model), in a microscale perturbed environment with controllable and realistic conditions. The spontaneous formation of different moving states and dynamic transitions between these states is observed. Additionally, the high robustness of the active-particle group at the critical density under the influence of different perturbations is quantitatively validated. These findings support the effectiveness of velocity alignment in real perturbed environments, thereby providing a versatile platform for fundamental studies on collective motion and the development of innovative swarm microrobotics.

Global temperature homogenization can obliterate temporal isolation in migratory animals with potential loss of population structure

Climate change is expected to increase the spatial autocorrelation of temperature, resulting in greater synchronization of climate variables worldwide. Possibly such 'homogenization of the world' leads to elevated risks of extinction and loss of biodiversity. In this study, we develop an empirical example on how increasing synchrony of global temperatures can affect population structure in migratory animals. We studied two subspecies of bar-tailed godwits Limosa lapponica breeding in tundra regions in Siberia: yamalensis in the west and taymyrensis further east and north. These subspecies share pre- and post-breeding stopover areas, thus being partially sympatric, but exhibiting temporal segregation. The latter is believed to facilitate reproductive isolation. Using satellite tracking data, we show that migration timing of both subspecies is correlated with the date of snowmelt in their respective breeding sites (later at the taymyrensis breeding range). Snow-cover satellite images demonstrate that the breeding ranges are on different climate trajectories and become more synchronized over time: between 1997 and 2020, the date of snowmelt advanced on average by 0.5 days/year in the taymyrensis breeding range, while it remained stable in the yamalensis breeding range. Previous findings showed how taymyrensis responded to earlier snowmelt by advancing arrival and clutch initiation. In the predicted absence of such advancements in yamalensis, we expect that the two populations will be synchronized by 2036-2040. Since bar-tailed godwits are social migrants, this raises the possibility of population exchange and prompts the question whether the two subspecies can maintain their geographic and morphological differences and population-specific migratory routines. The proposed scenario may apply to a wide range of (social) migrants as temporal segregation is crucial for promoting and maintaining reproductive isolation in many (partially sympatric) migratory populations. Homogenization of previously isolated populations could be an important consequence of increasing synchronized environments and hence climate change.

Early and accumulated experience shape migration and flight in Egyptian vultures

Two types of experience affect animals' behavioral proficiencies and, accordingly, their fitness: early-life experience, an animal's environment during its early development, and acquired experience, the repeated practice of a specific task.1,2,3,4,5,6,7,8 Yet, how these two experience types and their interactions affect different proficiencies is still an open question. Here, we study the interactions between these two types of experience during migration, a critical and challenging period.9,10 We do so by comparing migratory proficiencies between birds with different early-life experiences and explain these differences by testing fine-scale flight mechanisms. We used data collected by GPS transmitters during 127 autumn migrations of 65 individuals to study the flight proficiencies of two groups of Egyptian vultures (Neophron percnopterus), a long-distance, soaring raptor.11,12 The two groups differed greatly in their early-life experience, one group being captive bred and the other wild hatched.13 Both groups improved their migratory performance with acquired experience, exhibiting shorter migration times, longer daily progress, and improved flight skills, specifically more efficient soaring-gliding behavior. The observed improvements were mostly apparent for captive-bred vultures, which were the least efficient during their first migration but were able to catch up in their migratory performance already in the second migration. Thus, we show how the strong negative effects of early-life experience were offset by acquired experience. Our findings uncover how the interaction between early-life and acquired experiences may shape animals' proficiencies and shed new light on the ontogeny of animal migration, suggesting possible effects of sensitive periods of learning on the acquisition of migratory skills.

Opposing Responses to Scarcity Emerge from Functionally Unique Sociality Drivers

AbstractFrom biofilms to whale pods, organisms across taxa live in groups, thereby accruing numerous diverse benefits of sociality. All social organisms, however, pay the inherent cost of increased resource competition. One expects that when resources become scarce, this cost will increase, causing group sizes to decrease. Indeed, this occurs in some species, but there are also species for which group sizes remain stable or even increase under scarcity. What accounts for these opposing responses? We present a conceptual framework, literature review, and theoretical model demonstrating that differing responses to sudden resource shifts can be explained by which sociality benefit exerts the strongest selection pressure on a particular species. We categorize resource-related benefits of sociality into six functionally distinct classes and model their effect on the survival of individuals foraging in groups under different resource conditions. We find that whether, and to what degree, the optimal group size (or correlates thereof) increases, decreases, or remains constant when resource abundance declines depends strongly on the dominant sociality mechanism. Existing data, although limited, support our model predictions. Overall, we show that across a wide diversity of taxa, differences in how group size shifts in response to resource declines can be driven by differences in the primary benefits of sociality.

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

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

The price of being late: short- and long-term consequences of a delayed migration timing

Choosing the right migration timing is critical for migrants because conditions encountered en route influence movement costs, survival, and, in social migrants, the availability of social information. Depending on lifetime stages, individuals may migrate at different times due to diverging constraints, affecting the composition of migration groups. To examine the consequences of a delayed migration timing, we artificially delayed the migration of juvenile white storks (Ciconia ciconia) and thereby altered their physical and social environment. Using nearly continuous 1 Hz GPS trajectories, we examined their migration behaviour, ranging from sub-second level performance to global long-distance movement, in relation to two control groups. We found that delayed storks experienced suboptimal soaring conditions, but better wind support and thereby achieved higher flight speeds than control storks. Delayed storks had a lower mortality rate than the control storks and wintered closer to the breeding area. In fact, none of the delayed storks reached the traditional African wintering areas. Thus, our results show that juvenile storks can survive migrating at the 'wrong' time. However, this had long-term consequences on migration decisions. We suggest that, when timing their migration, storks balance not just energy and time, but also the availability of social information.

Environmental Path-Entropy and Collective Motion

Inspired by the swarming or flocking of animal systems we study groups of agents moving in unbounded 2D space. Individual trajectories derive from a "bottom-up" principle: individuals reorient to maximize their future path entropy over environmental states. This can be seen as a proxy for keeping options open, a principle that may confer evolutionary fitness in an uncertain world. We find an ordered (coaligned) state naturally emerges, as well as disordered states or rotating clusters; similar phenotypes are observed in birds, insects, and fish, respectively. The ordered state exhibits an order-disorder transition under two forms of noise: (i) standard additive orientational noise, applied to the postdecision orientations and (ii) "cognitive" noise, overlaid onto each individual's model of the future paths of other agents. Unusually, the order increases at low noise, before later decreasing through the order-disorder transition as the noise increases further.

Wild animals suppress the spread of socially transmitted misinformation

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

Emerging technologies for behavioral research in changing environments

The first response exhibited by animals to changing environments is typically behavioral. Behavior is thus central to predicting, and mitigating, the impacts that natural and anthropogenic environmental changes will have on populations and, consequently, ecosystems. Yet the inherently multiscale nature of behavior, as well as the complexities associated with inferring how animals perceive their world, and make decisions, has constrained the scope of behavioral research. Major technological advances in electronics and in machine learning, however, provide increasingly powerful means to see, analyze, and interpret behavior in its natural complexity. We argue that these disruptive technologies will foster new approaches that will allow us to move beyond quantitative descriptions and reveal the underlying generative processes that give rise to behavior.

Satellite Tracking of Head-Started Juvenile Green Turtles (Chelonia mydas) Reveals Release Effects and an Ontogenetic Shift

Juveniles of marine species, such as sea turtles, are often understudied in movement ecology. To determine dispersal patterns and release effects, we released 40 satellite-tagged juvenile head-started green turtles (Chelonia mydas, 1–4 years) from two separate locations (January and July 2023) off the coast of the Cayman Islands. A statistical model and vector plots were used to determine drivers of turtle directional swimming persistence and the role of ocean current direction. More than half (N = 22) effectively dispersed in 6–22 days from the islands to surrounding areas. The January turtles radiated out (185–1138 km) in distinct directions in contrast to the northward dispersal of the July turtles (27–396 km). Statistical results and vector plots supported that daily swimming persistence increased towards the end of tracks and near coastal regions, with turtles largely swimming in opposition to ocean currents. These results demonstrate that captive-reared juvenile greens have the ability to successfully navigate towards key coastal developmental habitats. Differences in dispersal (January vs. July) further support the importance of release timing and location. Our results inform conservation of the recovering Caymanian green turtles and we advise on how our methods can be improved and modified for future sea turtle and juvenile movement ecology studies.

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

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

Against the flow: unexpected migration movements over the open sea by inexperienced ospreys

As part of a long-term monitoring program, more than 80 Mediterranean ospreys Pandion haliaetus (both adults and juveniles) were tagged with GPS-GSM transmitters and tracked to study their spatiotemporal behaviour. Here we document the peculiar and unexpected migration movements performed by three inexperienced (juvenile/immature) individuals, who crossed the open sea “against the flow”, in the opposite direction to that foreseen for the given season. Using a combination of GPS tracking data and weather information, we found that such movements were linked to particular meteorological conditions occurring over the Mediterranean Sea during migration. Mean values of wind gust of approximately 20 km/h and moderate tailwinds seem to have mediated the onset of the movements, facilitating the flight of ospreys over water. Our findings suggest that both weather conditions (sidewinds) and the inexperience of the birds explain these long migration movements performed towards unexpected directions over the open sea. We conclude that migratory capabilities and the ability to cope with external conditions may lead inexperienced birds to perform extensive and tortuous dispersal/explotrative movements during both first autumn and spring migration.

Experience does not change the importance of wind support for migratory route selection by a soaring bird

Migration is a complex behaviour that is costly in terms of time, energy and risk of mortality. Thermal soaring birds rely on airflow, specifically wind support and uplift, to offset their energetic costs of flight. Their migratory routes are a record of movement decisions to negotiate the atmospheric environment and achieve efficiency. We expected that, regardless of age, birds use wind support to select their routes. Because thermal soaring is a complex flight behaviour that young birds need to learn, we expected that, as individuals gain more experience, their movement decisions will also increasingly favour the best thermal uplift conditions. We quantified how route choice during autumn migration of young European honey buzzards (Pernis apivorus) was adjusted to wind support and uplift over up to 4 years of migration and compared this with the choices of adult birds. We found that wind support was important in all migrations. However, we did not find an increase in the use of thermal uplifts. This could be due to the species-specific learning period and/or an artefact of the spatio-temporal scale of our uplift proxies.

Opportunistic soaring by birds suggests new opportunities for atmospheric energy harvesting by flying robots

The use of flying robots (drones) is increasing rapidly, but their utility is limited by high power demand, low specific energy storage and poor gust tolerance. By contrast, birds demonstrate long endurance, harvesting atmospheric energy in environments ranging from cluttered cityscapes to open landscapes, coasts and oceans. Here, we identify new opportunities for flying robots, drawing upon the soaring flight of birds. We evaluate mechanical energy transfer in soaring from first principles and review soaring strategies encompassing the use of updrafts (thermal or orographic) and wind gradients (spatial or temporal). We examine the extent to which state-of-the-art flying robots currently use each strategy and identify several untapped opportunities including slope soaring over built environments, thermal soaring over oceans and opportunistic gust soaring. In principle, the energetic benefits of soaring are accessible to flying robots of all kinds, given atmospherically aware sensor systems, guidance strategies and gust tolerance. Hence, while there is clear scope for specialist robots that soar like albatrosses, or which use persistent thermals like vultures, the greatest untapped potential may lie in non-specialist vehicles that make flexible use of atmospheric energy through path planning and flight control, as demonstrated by generalist flyers such as gulls, kites and crows.

Progresses of animal robots: A historical review and perspectiveness

Animal robots have remarkable advantages over traditional mechatronic ones in terms of energy supply, self-orientation, and natural concealment and can provide remarkable theoretical and practical values for scientific investigation, community service, military detection and other fields. Given these features, animal robots have become high-profile research objects and have recently attracted extensive attention. Herein, we have defined animal robots, reviewed the main types of animal robots, and discussed the potential developing directions. We have also detailed the mechanisms underlying the regulation of animal robots and introduced key methods for manipulating them. We have further proposed several application prospects for different types of animal robots. Finally, we have presented research directions for their further improvement.

Viewing animal migration through a social lens

Evidence of social learning is growing across the animal kingdom. Researchers have long hypothesized that social interactions play a key role in many animal migrations, but strong empirical support is scarce except in a few unique systems and species. In this review, we aim to catalyze advances in the study of social migrations by synthesizing research across disciplines and providing a framework for understanding when, how, and why social influences shape the decisions animals make during migration. Integrating research across the fields of social learning and migration ecology will advance our understanding of the complex behavioral phenomena of animal migration and help to inform conservation of animal migrations in a changing world.

Transition in collective motion decision making

Collective decision making in a biological motion group requires fast and robust transmission of information. Typically, directional switching information propagation across the whole group obeys a linear dispersion law. However, conventional dynamic collective motion models, such as the Vicsek model and the Couzin model did not take into account ultrafast directional synchronous motions. In the present paper, a multiparticle model is proposed based on inertial spin self-propel action, which can provide adequate description of such group motion. By considering both spin mechanism and collision avoidance, the proposed self-propelled particle spin model can nicely describe collective motion with fast directional switching. By analyzing the order parameter of the group-velocity synchronization, a mechanism of group decision making is revealed, which is based on the difference between two clusters of divergent leaders, showing a transition from the compromising phase (i.e., following the group average) to the preferred phase (i.e., aligning to a leader cluster). The finding provides new insight to the decision-making process of followers when they face with divergent leaders in group motion.

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

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

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

The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks

This is an essay advocating the efficacy of using the (noninteger) fractional calculus (FC) for the modeling of complex dynamical systems, specifically those pertaining to biomedical phenomena in general and oncological phenomena in particular. Herein we describe how the integer calculus (IC) is often incapable of describing what were historically thought to be simple linear phenomena such as Newton's law of cooling and Brownian motion. We demonstrate that even linear dynamical systems may be more accurately described by fractional rate equations (FREs) when the experimental datasets are inconsistent with models based on the IC. The Network Effect is introduced to explain how the collective dynamics of a complex network can transform a many-body noninear dynamical system modeled using the IC into a set of independent single-body fractional stochastic rate equations (FSREs). Note that this is not a mathematics paper, but rather a discussion focusing on the kinds of phenomena that have historically been approximately and improperly modeled using the IC and how a FC replacement of the model better explains the experimental results. This may be due to hidden effects that were not anticapated in the IC model, or to an effect that was acknowledged as possibly significant, but beyond the mathematical skills of the investigator to Incorporate into the original model. Whatever the reason we introduce the FRE used to describe mathematical oncology (MO) and review the quality of fit of such models to tumor growth data. The analytic results entailed in MO using ordinary diffusion as well as fractional diffusion are also briefly discussed. A connection is made between a time-dependent fractional-order derivative, technically called a distributed-order parameter, and the multifractality of time series, such that an observed multifractal time series can be modeled using a FRE with a distributed fractional-order derivative. This equivalence between multifractality and distributed fractional derivatives has not received the recognition in the applications literature we believe it warrants.

Paternal transmission of migration knowledge in a long-distance bird migrant

While advances in biologging have revealed many spectacular animal migrations, it remains poorly understood how young animals learn to migrate. Even in social species, it is unclear how migratory skills are transmitted from one generation to another and what implications this may have. Here we show that in Caspian terns Hydroprogne caspia family groups, genetic and foster male parents carry the main responsibility for migrating with young. During migration, young birds stayed close to an adult at all times, with the bond dissipating on the wintering grounds. Solo-migrating adults migrated faster than did adults accompanying young. Four young that lost contact with their parent at an early stage of migration all died. During their first solo migration, subadult terns remained faithful to routes they took with their parents as young. Our results provide evidence for cultural inheritance of migration knowledge in a long-distance bird migrant and show that sex-biased (allo)parental care en route shapes migration through social learning.

Quantification of Foraging Areas for the Northern Bald Ibis (Geronticus eremita) in the Northern Alpine Foothills: A Random Forest Model Fitted with Optical and Actively Sensed Earth Observation Data

The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios.

A physics perspective on collective animal behavior

The beautiful dynamic patterns and coordinated motion displayed by groups of social animals are a beautiful example of self-organization in natural farfrom-equilibrium systems. Recent advances in active-matter physics have enticed physicists to begin to consider how their results can be extended from microscale physical or biological systems to groups of real, macroscopic animals. At the same time, advances in measurement technology have led to the increasing availability of high-quality empirical data for the behavior of animal groups both in the laboratory and in the wild. In this review, I survey this available data and the ways that it has been analyzed. I then describe how physicists have approached synthesizing, modeling, and interpreting this information, both at the level of individual animals and at the group scale. In particular, I focus on the kinds of analogies that physicists have made between animal groups and more traditional areas of physics.

Order and information in the patterns of spinning magnetic micro-disks at the air-water interface

The application of the Shannon entropy to study the relationship between information and structures has yielded insights into molecular and material systems. However, the difficulty in directly observing and manipulating atoms and molecules hampers the ability of these systems to serve as model systems for further exploring the links between information and structures. Here, we use, as a model experimental system, hundreds of spinning magnetic micro-disks self-organizing at the air-water interface to generate various spatiotemporal patterns with varying degrees of order. Using the neighbor distance as the information-bearing variable, we demonstrate the links among information, structure, and interactions. We establish a direct link between information and structure without using explicit knowledge of interactions. Last, we show that the Shannon entropy by neighbor distances is a powerful observable in characterizing structural changes. Our findings are relevant for analyzing natural self-organizing systems and for designing collective robots.

Network analysis of intra- and interspecific freshwater fish interactions using year-around tracking

A long-term, yet detailed view into the social patterns of aquatic animals has been elusive. With advances in reality mining tracking technologies, a proximity-based social network (PBSN) can capture detailed spatio-temporal underwater interactions. We collected and analysed a large dataset of 108 freshwater fish from four species, tracked every few seconds over 1 year in their natural environment. We calculated the clustering coefficient of minute-by-minute PBSNs to measure social interactions, which can happen among fish sharing resources or habitat preferences (positive/neutral interactions) or in predator and prey during foraging interactions (agonistic interactions). A statistically significant coefficient compared to an equivalent random network suggests interactions, while a significant aggregated clustering across PBSNs indicates prolonged, purposeful social behaviour. Carp (Cyprinus carpio) displayed within- and among-species interactions, especially during the day and in the winter, while tench (Tinca tinca) and catfish (Silurus glanis) were solitary. Perch (Perca fluviatilis) did not exhibit significant social behaviour (except in autumn) despite being usually described as a predator using social facilitation to increase prey intake. Our work illustrates how methods for building a PBSN can affect the network's structure and highlights challenges (e.g. missing signals, different burst frequencies) in deriving a PBSN from reality mining technologies.

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

Cognition, defined as the processes concerned with the acquisition, retention and use of information, underlies animals’ abilities to navigate their local surroundings, embark on long-distance seasonal migrations, and socially learn information relevant to movement. Hence, in order to fully understand and predict animal movement, researchers must know the cognitive mechanisms that generate such movement. Work on a few model systems indicates that most animals possess excellent spatial learning and memory abilities, meaning that they can acquire and later recall information about distances and directions among relevant objects. Similarly, field work on several species has revealed some of the mechanisms that enable them to navigate over distances of up to several thousand kilometers. Key behaviors related to movement such as the choice of nest location, home range location and migration route are often affected by parents and other conspecifics. In some species, such social influence leads to the formation of aggregations, which in turn may lead to further social learning about food locations or other resources. Throughout the review, we note a variety of topics at the interface of cognition and movement that invite further investigation. These include the use of social information embedded in trails, the likely important roles of soundscapes and smellscapes, the mechanisms that large mammals rely on for long-distance migration, and the effects of expertise acquired over extended periods.

Coordinating directional switches in pigeon flocks: the role of nonlinear interactions

The mechanisms inducing unpredictably directional switches in collective and moving biological entities are largely unclear. Deeply understanding such mechanisms is beneficial to delicate design of biologically inspired devices with particular functions. Here, articulating a framework that integrates data-driven, analytical and numerical methods, we investigate the underlying mechanism governing the coordinated rotational flight of pigeon flocks with unpredictably directional switches. Particularly using the sparse Bayesian learning method, we extract the inter-agent interactional dynamics from the high-resolution GPS data of three pigeon flocks, which reveals that the decision-making process in rotational switching flight performs in a more nonlinear manner than in smooth coordinated flight. To elaborate the principle of this nonlinearity of interactions, we establish a data-driven particle model with two potential wells and estimate the mean switching time of rotational direction. Our model with its analytical and numerical results renders the directional switches of moving biological groups more interpretable and predictable. Actually, an appropriate combination of natures, including high density, stronger nonlinearity in interactions, and moderate strength of noise, can enhance such highly ordered, less frequent switches.

Animal tag technology keeps coming of age: an engineering perspective

Animal-borne tags (biologgers) have now become extremely sophisticated, recording data from multiple sensors at high frequencies for long periods and, as such, have become a powerful tool for behavioural ecologists and physiologists studying wild animals. But the design and implementation of these tags is not trivial because engineers have to maximize performance and ability to function under onerous conditions while minimizing tag mass and volume (footprint) to maximize the wellbeing of the animal carriers. We present some of the major issues faced by tag engineers and show how tag designers must accept compromises while maintaining systems that can answer the questions being posed. We also argue that basic understanding of engineering issues in tag design by biologists will help feedback to engineers to better tag construction but also reduce the likelihood that tag-deploying biologists will misunderstand their own results. Finally, we suggest that proper consideration of conventional technology together with new approaches will lead to further step changes in our understanding of wild-animal biology using smart tags. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Smell of green leaf volatiles attracts white storks to freshly cut meadows

Finding food is perhaps the most important task for all animals. Birds often show up unexpectedly at novel food sources such as freshly tilled fields or mown meadows. Here we test whether wild European white storks primarily use visual, social, auditory or olfactory information to find freshly cut farm pastures where insects and rodents abound. Aerial observations of an entire local stork population documented that birds could not have become aware of a mown field through auditory, visual or social information. Only birds within a 75° downwind cone over 0.4–16.6 km approached any mown field. Placing freshly cut grass from elsewhere on selected unmown fields elicited similarly immediate stork approaches. Furthermore, uncut fields that were sprayed with a green leaf volatile organic compound mix ((Z)-3-hexenal, (Z)-3-hexenol, hexenyl acetate), the smell of freshly cut grass, immediately attracted storks. The use of long-distance olfactory information for finding food may be common in birds, contrary to current perception.

Visual analytics of sensor movement data for cheetah behaviour analysis

Abstract

Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments.

Graphic abstract

Fish as a model in social neuroscience: conservation and diversity in the social brain network

Mechanisms for fish social behaviours involve a social brain network (SBN) which is evolutionarily conserved among vertebrates. However, considerable diversity is observed in the actual behaviour patterns amongst nearly 30000 fish species. The huge variation found in socio-sexual behaviours and strategies is likely generated by a morphologically and genetically well-conserved small forebrain system. Hence, teleost fish provide a useful model to study the fundamental mechanisms underlying social brain functions. Herein we review the foundations underlying fish social behaviours including sensory, hormonal, molecular and neuroanatomical features. Gonadotropin-releasing hormone neurons clearly play important roles, but the participation of vasotocin and isotocin is also highlighted. Genetic investigations of developing fish brain have revealed the molecular complexity of neural development of the SBN. In addition to straightforward social behaviours such as sex and aggression, new experiments have revealed higher order and unique phenomena such as social eavesdropping and social buffering in fish. Finally, observations interpreted as 'collective cognition' in fish can likely be explained by careful observation of sensory determinants and analyses using the dynamics of quantitative scaling. Understanding of the functions of the SBN in fish provide clues for understanding the origin and evolution of higher social functions in vertebrates.

Consensus of travel direction is achieved by simple copying, not voting, in free-ranging goats

For group-living animals to remain cohesive they must agree on where to travel. Theoretical models predict shared group decisions should be favoured, and a number of empirical examples support this. However, the behavioural mechanisms that underpin shared decision-making are not fully understood. Groups may achieve consensus of direction by active communication of individual preferences (i.e. voting), or by responding to each other's orientation and movement (i.e. copying). For example, African buffalo (Syncerus caffer) are reported to use body orientation to vote and indicate their preferred direction to achieve a consensus on travel direction, while golden shiners (Notemigonus crysoleucas) achieve consensus of direction by responding to the movement cues of their neighbours. Here, we present a conceptual model (supported by agent-based simulations) that allows us to distinguish patterns of motion that represent voting or copying. We test our model predictions using high-resolution GPS and magnetometer data collected from a herd of free-ranging goats (Capra aegagrus hircus) in the Namib Desert, Namibia. We find that decisions concerning travel direction were more consistent with individuals copying one another's motion and find no evidence to support the use of voting with body orientation. Our findings highlight the role of simple behavioural rules for collective decision-making by animal groups.

Hierarchical Approach for Comparing Collective Behavior Across Scales: Cellular Systems to Honey Bee Colonies

How individuals in a group lead to collective behavior is a fundamental question across biological systems, from cellular systems, to animal groups, to human organizations. Recent technological advancements have enabled an unprecedented increase in our ability to collect, quantify, and analyze how individual responses lead to group behavior. However, despite a wealth of data demonstrating that collective behavior exists across biological scales, it is difficult to make general statements that apply in different systems. In this perspective, we present a cohesive framework for comparing groups across different levels of biological organization, using an intermediate link of “collective mechanisms” that connects individual responses to group behavior. Using this approach we demonstrate that an effective way of comparing different groups is with an analysis hierarchy that asks complementary questions, including how individuals in a group implement various collective mechanisms, and how these various mechanisms are used to achieve group function. We apply this framework to compare two collective systems—cellular systems and honey bee colonies. Using a case study of a response to a disturbance, we compare and contrast collective mechanisms used in each system. We then discuss how inherent differences in group structure and physical constraints lead to different combinations of collective mechanisms to solve a particular problem. Together, we demonstrate how a hierarchical approach can be used to compare and contrast different systems, lead to new hypotheses in each system, and form a basis for common research questions in collective behavior.

Exploratory study of introducing HPC to non-ICT researchers: institutional strategy is possibly needed for widespread adaption

Machine learning algorithms are becoming more and more useful in many fields of science, including many areas where computational methods are rarely used. High-performance Computing (HPC) is the most powerful solution to get the best results using these algorithms. HPC requires various skills to use. Acquiring this knowledge might be intimidating and take a long time for a researcher with small or no background in information and communications technologies (ICTs), even if the benefits of such knowledge is evident for the researcher. In this work, we aim to assess how a specific method of introducing HPC to such researchers enables them to start using HPC. We gave talks to two groups of non-ICT researchers that introduced basic concepts focusing on the necessary practical steps needed to use HPC on a specific cluster. We also offered hands-on trainings for one of the groups which aimed to guide participants through the first steps of using HPC. Participants filled out questionnaires partly based on Kirkpatrick's training evaluation model before and after the talk, and after the hands-on training. We found that the talk increased participants' self-reported likelihood of using HPC in their future research, but this was not significant for the group where participation was voluntary. On the contrary, very few researchers participated in the hands-on training, and for these participants neither the talk, nor the hands-on training changed their self-reported likelihood of using HPC in their future research. We argue that our findings show that academia and researchers would benefit from an environment that not only expects researchers to train themselves, but provides structural support for acquiring new skills.

Less is more: On-board lossy compression of accelerometer data increases biologging capacity

GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission.

Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity.

We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods.

We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals’ time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals.

In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study situations where limitations in energy and storage space of the device pose a problem. Ultimately, these developments can allow for long‐term and nearly continuous remote monitoring of the behaviour of free‐ranging animals.

Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures

Automated monitoring of the movements and behaviour of animals is a valuable research tool. Recently, machine learning tools were applied to many species to classify units of behaviour. For the monitoring of wild species, collecting enough data for training models might be problematic, thus we examine how machine learning models trained on one species can be applied to another closely related species with similar behavioural conformation. We contrast two ways to calculate accuracies, termed here as overall and threshold accuracy, because the field has yet to define solid standards for reporting and measuring classification performances. We measure 21 dogs and 7 wolves, and find that overall accuracies are between 51 and 60% for classifying 8 behaviours (lay, sit, stand, walk, trot, run, eat, drink) when training and testing data are from the same species and between 41 and 51% when training and testing is cross-species. We show that using data from dogs to predict the behaviour of wolves is feasible. We also show that optimising the model for overall accuracy leads to similar overall and threshold accuracies, while optimizing for threshold accuracy leads to threshold accuracies well above 80%, but yielding very low overall accuracies, often below the chance level. Moreover, we show that the most common method for dividing the data between training and testing data (random selection of test data) overestimates the accuracy of models when applied to data of new specimens. Consequently, we argue that for the most common goals of animal behaviour recognition overall accuracy should be the preferred metric. Considering, that often the goal is to collect movement data without other methods of observation, we argue that training data and testing data should be divided by individual and not randomly.