Search

Bird migrations are impressive behavioral phenomena, representing complex spatiotemporal strategies to balance costs of living while maximizing fitness. The field of bird migration research has made great strides over the past decades, yet fundamental gaps remain. Technologies have sparked a transformation in the study of bird migration research by revealing remarkable insights into the underlying behavioral, cognitive, physiological and evolutionary mechanisms of these diverse journeys. Here, we aim to encourage broad discussions and promote future studies by highlighting research fields that are characterized by major knowledge gaps or conflicting evidence, namely the fields of navigation, social learning, individual development, energetics and conservation. We approach each topic by summarizing the current state of knowledge and provide a future outlook of ideas and state-of-the-art methods to further advance the field. Integrating knowledge across these disciplines will allow us to understand the adaptive abilities of different species and to develop effective conservation strategies in a rapidly changing world.

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change. Publisher PDF

Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging.Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head‐mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head “movement” is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing (“environmental framing”).We propose a new approach to visualize the data of such head‐mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by “longitude” position and head pitch by “latitude” on this “orientation sphere” (O‐sphere).We construct the O‐sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading‐independent head rotation, and propose the derivation of O‐sphere‐metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors.Visualizations of the O‐sphere provide an intuitive representation of animal behavior manifest via head orientation and rotation. This has ramifications for quantifying and understanding behaviors ranging from navigation through vigilance to feeding and, when used in tandem with body movement, should provide an important link between perception of the environment and response to it in free‐ranging animals., We provide a new visualization of animal body and head orientation that shows in an intuitive manner how the animal frames the environment. Metrics derived from this should help us understand how animals make decisions to adopt particular behaviours.

Background Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies.

Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

Physical energy defines the energy landscape and determines the species-specific cost of movement, thus influencing movement decisions. In unpredictable and dynamic environments, observing the locomotion of others increases individual certainty in the distribution of physical energy to increase movement efficiency. Beyond the physical energy landscape, social sampling increases certainty in all ecological landscapes that influence animal movement (including fear and resource landscapes), and individuals use energy to express each of these. We call for the development of an 'optimal movement theory' (OMT) that integrates the multidimensional reality of movement decisions by combining ecological landscapes according to a single expectation of energy cost-benefit, where social sampling provides up-to-date information under uncertain conditions. This mechanistic framework has implications for predicting individual movement patterns and for investigating the emergence of aggregations. published

Quantum simulation using synthetic systems is a promising route to solve outstanding quantum many-body problems in regimes where other approaches, including numerical ones, fail

Significance Flapping flight is extremely costly for large birds, yet little is known about the conditions that force them to flap. We attached custom-made “flight recorders” to Andean condors, the world’s heaviest soaring birds, documenting every single wingbeat and when and how individuals gained altitude. Remarkably, condors flapped for only 1% of their flight time, specifically during takeoff and when close to the ground. This is particularly striking as the birds were immature. Thus, our results demonstrate that even inexperienced birds can cover vast distances over land without flapping. Overall, this can help explain how extinct birds with twice the wingspan of condors could have flown., Flight costs are predicted to vary with environmental conditions, and this should ultimately determine the movement capacity and distributions of large soaring birds. Despite this, little is known about how flight effort varies with environmental parameters. We deployed bio-logging devices on the world’s heaviest soaring bird, the Andean condor (Vultur gryphus), to assess the extent to which these birds can operate without resorting to powered flight. Our records of individual wingbeats in >216 h of flight show that condors can sustain soaring across a wide range of wind and thermal conditions, flapping for only 1% of their flight time. This is among the very lowest estimated movement costs in vertebrates. One bird even flew for >5 h without flapping, covering ∼172 km. Overall, > 75% of flapping flight was associated with takeoffs. Movement between weak thermal updrafts at the start of the day also imposed a metabolic cost, with birds flapping toward the end of glides to reach ephemeral thermal updrafts. Nonetheless, the investment required was still remarkably low, and even in winter conditions with weak thermals, condors are only predicted to flap for ∼2 s per kilometer. Therefore, the overall flight effort in the largest soaring birds appears to be constrained by the requirements for takeoff.

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models. Fil: Williams, Hannah J.. Swansea University; Reino Unido Fil: Taylor, Lucy. University of Oxford; Reino Unido. Save The Elephants; Kenia Fil: Benhamou, Simon. Centre National de la Recherche Scientifique; Francia Fil: Bijleveld, Allert. Utrecht University; Países Bajos Fil: Clay, Thomas. University of Liverpool; Reino Unido Fil: de Grissac, Sophie. Swansea University; Reino Unido Fil: Demsar, Urska. University of St. Andrews; Reino Unido Fil: English, Holly M.. Swansea University; Reino Unido Fil: Franconi, Novella. Swansea University; Reino Unido Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Griffiths, Rachael. Swansea University; Reino Unido Fil: Kay, William P.. Swansea University; Reino Unido Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina Fil: Potts, Jonathan. University of Sheffield; Reino Unido Fil: Rogerson, Katharine F.. University of East Anglia; Reino Unido Fil: Rutz, Christian. University of St. Andrews; Reino Unido Fil: Spelt, Anouk. University of Bristol; Reino Unido Fil: Trevail, Alice. University of Liverpool; Reino Unido Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Börger, Luca. Swansea University; Reino Unido

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal-attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement-based power. Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings. Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement-related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non-movement-related factors, such as heat production, increases. Overall, DBA seems to be a substantive proxy for movement-based power but consideration of other movement-related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement-based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration. Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Börger, Luca. Swansea University; Reino Unido Fil: Holton, Mark. Swansea University; Reino Unido Fil: Scantlebury, D. Michael. The Queens University of Belfast; Irlanda Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Rosell, Frank. University of South‐Eastern Norway; Noruega Fil: Graf, Patricia M.. Universitat Fur Bodenkultur Wien; Austria. University of Ljubljana; Eslovenia Fil: Williams, Hannah. Swansea University; Reino Unido Fil: Gunner, Richard. Swansea University; Reino Unido Fil: Hopkins, Lloyd. Swansea University; Reino Unido Fil: Marks, Nikki. The Queens University of Belfast; Irlanda Fil: Geraldi, Nathan R.. King Abdullah University of Science and Technology; Arabia Saudita Fil: Duarte, Carlos M.. King Abdullah University of Science and Technology; Arabia Saudita Fil: Scott, Rebecca. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Strano, Michael S.. Massachusetts Institute of Technology; Estados Unidos Fil: Robotka, Hermina. Max Planck Institute für Ornithologie; Alemania Fil: Eizaguirre, Christophe. , Queen Mary University of London; Reino Unido Fil: Fahlman, Andreas. undación Oceanogràfic de la Comunidad Valenciana; España Fil: Shepard, Emily L. C.. Swansea University; Reino Unido. Max Planck Institute für Ornithologie; Alemania

Thus far, ecophysiology research has predominantly been conducted within controlled laboratory-based environments, owing to a mismatch between the recording technologies available for physiological monitoring in wild animals and the suite of behaviours and environments they need to withstand, without unduly affecting subjects. While it is possible to record some physiological variables for free-living animals using animal-attached logging devices, including inertial-measurement, heart-rate and temperature loggers, the field is still in its infancy. In this opinion piece, we review the most important future research directions for advancing the field of ‘physiologging’ in wild animals, including the technological development that we anticipate will be required, and the fiscal and ethical challenges that must be overcome. Non-invasive, multi-sensor miniature devices are ubiquitous in the world of human health and fitness monitoring, creating invaluable opportunities for animal and human physiologging to drive synergistic advances. We argue that by capitalizing on the research efforts and advancements made in the development of human wearables, it will be possible to design the non-invasive loggers needed by ecophysiologists to collect accurate physiological data from free-ranging animals ethically and with an absolute minimum of impact. In turn, findings have the capacity to foster transformative advances in human health monitoring. Thus, we invite biomedical engineers and researchers to collaborate with the animal-tagging community to drive forward the advancements necessary to realize the full potential of both fields. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.

Funding: UK Natural Environment Research Council (NERC Standard Grant NE/H007466/1). SB is currently funded by an ERC consolidator’s grant (310820: STATEMIG). EW is funded by the NERC (Independent Research Fellowship NE/M017990/1). Migration is a fundamental behavioral process prevalent among a wide variety of animal taxa. As individuals are increasingly shown to present consistent responses to environmental cues for breeding or foraging, it may be expected that approaches to migration would present similar among-individual consistencies. Seabirds frequently show consistent individual differences in a range of traits related to foraging and space-use during both the breeding and non-breeding seasons, but the causes and consequences of this consistency are poorly understood. In this study, we combined analysis of geolocation and stable isotope data across multiple years to investigate individual variation in the non-breeding movements and diets of northern gannets Morus bassanus, and the consequences for changes in body condition. We found that individuals were highly repeatable in their non-breeding destination over consecutive years even though the population-level non-breeding distribution spanned >35° of latitude. Isotopic signatures were also strongly repeatable, with individuals assigned to one of two dietary clusters defined by their distinct trophic (δ15N) and spatial (δ13C) position. The only non-breeding destination in which the two dietary clusters co-occurred was off the coast of northwest Africa. The majority of individuals adopted a consistent foraging strategy, as they remained within the same dietary cluster across years, with little variation in body mass corrected for size among these consistent individuals. In contrast, the few individuals that switched clusters between years were in better condition relative to the rest of the population, suggesting there may be benefits to flexibility during the non-breeding period. Our results indicate that a consistent migratory strategy can be effective regardless of wintering region or diet, but that there may be benefits to those individuals able to display flexibility. This appears to be an important behavioral strategy that may enhance individual condition. Publisher PDF

International audience; Many large birds rely on thermal soaring flight to travel crosscountry. As such, they are under selective pressure to minimise the time spent gaining altitude in thermal updrafts. Birds should be able to maximise their climb rates by maintaining a position close to the thermal core through careful selection of bank angle and airspeed; however, there have been few direct measurements of either parameter. Here, we apply a novel methodology to quantify the bank angles selected by soaring birds using on-board magnetometers. We couple these data with airspeed measurements to parameterise the soaring envelope of two species of Gyps vulture, from which it is possible to predict 'optimal' bank angles. Our results show that these large birds respond to the challenges of gaining altitude in the initial phase of the climb, where thermal updrafts are weak and narrow, by adopting relatively high, and conserved, bank angles (25-35 deg). The bank angle decreased with increasing altitude, in a manner that was broadly consistent with a strategy of maximising the rate of climb. However, the lift coefficients estimated in our study were lower than those predicted by theoretical models and wind-tunnel studies. Overall, our results highlight how the relevant currency for soaring performance changes within individual climbs: when thermal radius is limiting, birds vary bank angle and maintain a constant airspeed, but speed increases later in the climb in order to respond to decreasing air density.

The development of multisensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. Overall behaviour recognition using our new approach was better than most other methods due to; (1) its ability to deal with behavioural variation and (2) the speed with which the task was completed because extraneous data are avoided in the process. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work. Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Holton, Mark. Swansea University; Reino Unido Fil: Di Virgilio, Agustina Soledad. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentina Fil: Williams, Hannah. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University; Reino Unido Fil: Lambertucci, Sergio Agustin. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentina Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Sala, Juan Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Balaji, Bharathan. University of California at Los Angeles; Estados Unidos Fil: Lee, Eun Sun. University of California at Los Angeles; Estados Unidos Fil: Srivastava, Mani. University of California at Los Angeles; Estados Unidos Fil: Scantlebury, D. Michael. The Queens University of Belfast; Irlanda Fil: Duarte, Carlos M.. King Abdullah University Of Science And Technology

Biologists studying animals in their natural environment are increasingly using sensors such as accelerometers in animal-attached `smart' tags because it is widely acknowledged that this approach can enhance the understanding of ecological and behavioural processes. The potential of such tags is tempered by the difficulty of extracting animal behaviour from the sensors which is currently primarily dependent on the manual inspection of multiple time series graphs. This is time consuming and error-prone for the domain expert and is now the limiting factor for realising the value of tags in this area. We introduce TimeClassifier, a visual analytic system for the classification of time series data for movement ecologists. We deploy our system with biologists and report two real-world case studies of its use.

Background: Accelerometry has been used to identify behaviours through the quantification of body posture and motion for a range of species moving in different media. This technique has not been applied to flight behaviours to the same degree, having only been used to distinguish flapping from soaring flight, even though identifying the type of soaring flight could provide important insights into the factors underlying movement paths in soaring birds. This may be due to the complexities of interpreting acceleration data, as movement in the aerial environment may be influenced by phenomena such as centripetal acceleration (pulling-g). This study used high-resolution movement data on the flight of free-living Andean condors (Vultur gryphus) and a captive Eurasian griffon vulture (Gyps fulvus) to examine the influence of gravitational, dynamic and centripetal acceleration in different flight types. Flight behaviour was categorised as thermal soaring, slope soaring, gliding and flapping, using changes in altitude and heading from magnetometry data. We examined the ability of the k-nearest neighbour (KNN) algorithm to distinguish between these behaviours using acceleration data alone. Results: Values of the vectorial static body acceleration (VeSBA) suggest that these birds experience relatively little centripetal acceleration in flight, though this varies between flight types. Centripetal acceleration appears to be of most influence during thermal soaring; consequently, it is not possible to derive bank angle from smoothed values of lateral acceleration. In contrast, the smoothed acceleration values in the dorso-ventral axis provide insight into body pitch, which varied linearly with airspeed. Classification of passive flight types via KNN was limited, with low accuracy and precision for soaring and gliding. Conclusion: The importance of soaring was evident in the high proportion of time each bird spent in this flight mode (52.17–84.00 %). Accelerometry alone was limited in its ability to distinguish between passive flight types, though smoothed values in the dorso-ventral axis did vary with airspeed. Other sensors, in particular the magnetometer, provided powerful methods of identifying flight behaviour and these data may be better suited for automated behavioural identification. This should provide further insight into the type and strength of updraughts available to soaring birds. Fil: Williams, H. J.. Swansea University; Reino Unido Fil: Shepard, E. L. C.. Swansea University; Reino Unido Fil: Duriez, O.. Universite Montpellier Ii; Francia Fil: Lambertucci, Sergio Agustin. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina

Vultures are thought to form networks in the sky, with individuals monitoring the movements of others to gain up-to-date information on resource availability. While it is recognized that social information facilitates the search for carrion, how this facilitates the search for updrafts, another critical resource, remains unknown. In theory, birds could use information on updraft availability to modulate their flight speed, increasing their airspeed when informed on updraft location. In addition, the stylized circling behaviour associated with thermal soaring is likely to provide social cues on updraft availability for any bird operating in the surrounding area. We equipped fiveGypsvultures with GPS and airspeed loggers to quantify the movements of birds flying in the same airspace. Birds that were socially informed on updraft availability immediately adopted higher airspeeds on entering the inter-thermal glide; a strategy that would be risky if birds were relying on personal information alone. This was embedded within a broader pattern of a reduction in airspeed (approx. 3 m s−1) through the glide, likely reflecting the need for low speed to sense and turn into the next thermal. Overall, this demonstrates (i) the complexity of factors affecting speed selection over fine temporal scales and (ii) thatGypsvultures respond to social information on the occurrence of energy in the aerial environment, which may reduce uncertainty in their movement decisions.

Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals operate. However, interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. This work presents a single program, FRAMEWORK 4, that uses a particular sensor constellation described in the?Daily Diary? tag (recording tri-axial acceleration, tri-axial magnetic field intensity, pressure and e.g. temperature and light intensity) to determine the 4 key elements considered pivotal within the conception of the tag. These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal operates. The program takes the original data recorded by the Daily Dairy and transforms it into dead-reckoned movements,template-matched behaviours, dynamic body acceleration-derived energetics and positionlinked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space. Fil: Walker, James S.. Swansea University. College Of Sciences; Reino Unido Fil: Jones, Mark W.. Swansea University. College Of Sciences; Reino Unido Fil: Laramee, Robert S.. Swansea University. College Of Sciences; Reino Unido Fil: Holton, Mark D.. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University. College Of Sciences; Reino Unido Fil: Williams, Hannah J.. Swansea University. College Of Sciences; Reino Unido Fil: Scantlebury, D. Michael. The Queens University Of Belfast; Irlanda Fil: Marks, Nikki, J.. The Queens University Of Belfast; Irlanda Fil: Magowan, Elizabeth A.. The Queens University Of Belfast; Irlanda Fil: Maguire, Iain E.. The Queens University Of Belfast; Irlanda Fil: Grundy, Ed. Swansea University. College Of Sciences; Reino Unido Fil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue; Argentina Fil: Wilson, Rory P.. Swansea University. College Of Sciences; Reino Unido

Background Whether, and how, animals move requires them to assess their environment to determine the most appropriate action and trajectory, although the precise way the environment is scanned has been little studied. We hypothesized that head attitude, which effectively frames the environment for the eyes, and the way it changes over time, would be modulated by the environment. Method To test this, we used a head-mounted device (Human-Interfaced Personal Observation platform - HIPOP) on people moving through three different environments; a botanical garden (‘green’ space), a reef (‘blue’ space), and a featureless corridor, to examine if head movement in the vertical axis differed between environments. Template matching was used to identify and quantify distinct behaviours. Conclusions The data on head pitch from all subjects and environments over time showed essentially continuous clear waveforms with varying amplitude and wavelength. There were three stylised behaviours consisting of smooth, regular peaks and troughs in head pitch angle and variable length fixations during which the head pitch remained constant. These three behaviours accounted for ca. 40 % of the total time, with irregular head pitch changes accounting for the rest. There were differences in rates of manifestation of behaviour according to environment as well as environmentally different head pitch values of peaks, troughs and fixations. Finally, although there was considerable variation in head pitch angles, the peak and trough values bounded most of the variation in the fixation pitch values. It is suggested that the constant waveforms in head pitch serve to inform people about their environment, providing a scanning mechanism. Particular emphasis to certain sectors is manifest within the peak and trough limits and these appear modulated by the distribution of the points where fixation, interpreted as being due to objects of interest, occurs. This behaviour explains how animals allocate processing resources to the environment and shows promise for movement studies attempting to elucidate which parts of the environment affect movement trajectories.

Background: Accelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers. Methods: We calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.Results: Tri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading. Conclusion: Magnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry. Fil: Williams, Hannah J.. Swansea University; Reino Unido Fil: Holton, Mark D.. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University; Reino Unido Fil: Largey, Nicola. Swansea University; Reino Unido Fil: Norman, Brad. Ecocean Inc. (aust.); Australia Fil: Ryan, Peter G.. University of Cape Town; Sudáfrica Fil: Duriez, Olivier. Université Paul-Valéry Montpellier; Francia Fil: Scantlebury, Michael. The Queens University of Belfast; Irlanda Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Magowan, Elizabeth A.. The Queens University of Belfast; Irlanda Fil: Marks, Nikki J.. The Queens University of Belfast; Irlanda Fil: Alagaili, Abdulaziz N.. King Saud University; Arabia Saudita Fil: Bennett, Nigel C.. University of Pretoria; Sudáfrica Fil: Wilson, Rory P.. Swansea University; Reino Unido