Abstract Animal movement paths display substantial complexity and variability, promoting efforts to identify universal rules and models that best describe them. Using high‐resolution (≥10 Hz) movement from 43 vertebrate species spanning diverse taxa, body sizes, and lifestyles, we show that paths are universally composed of straight‐line steps interspersed with sharp turns, echoing patterns documented in lower taxa such as bacteria. We report how vertebrate “fundamental steps”—straight travel segments between successive detected turns (with F stepduration as the turn‐to‐turn interval and F steplength as the corresponding distance when displacement is available)—and “fundamental turn angles” ( F turnangles ; net changes in travel heading between successive steps) vary with species' mass, locomotor mode, behavior, and environment. Here, “fundamental” denotes the finest scale step/turn events resolvable under our sampling rate and turn‐detection criteria; these event‐scale steps/turns are intrinsically different from the straight‐line segments inferred from low‐resolution position data. To explain these relationships, we posit that animals inherently move in a straight line until sensory information signals a better heading, triggering a turn. Across all species examined, animals spent the vast majority of their travel time moving in straight lines (species‐level means >90%), with turns representing discrete decision points influenced by body size, locomotor mode, and ecological context. Larger animals turned less frequently, consistent with biomechanical constraints of mass and rotational inertia, while aerial species often exhibited higher turning rates driven by soaring flight demands. We further show that turns can be linked to diverse behavioral drivers, including prey pursuit, obstacle avoidance, predator evasion, and exploitation of environmental energy. By explicitly quantifying turns, we clarify how distributions of step durations and turn angles interact to shape movement patterns and why different statistical models (e.g., correlated random walks, Lévy flights) emerge when lower resolution data are analyzed. Finally, we demonstrate how fundamental steps and turns can be incorporated into an agent‐based modeling framework using penguins as a case study, enabling reconstruction of realistic tracks and prediction of movement responses to environmental change. Straight‐line travel punctuated by decision‐driven turns thus emerges as a fundamental principle of vertebrate movement, linking fine‐scale movement structure, ecological context, and emergent patterns of space use.

The study of animals' activity and behavior in the wild is an extremely challenging task. Although tri-axial accelerometers are invaluable for behavioral analyses, their use is more frequent in large charismatic endotherms with limited application in ectotherms. The scarce utilization of this methodology on small-size reptiles is focused on animals' activity and energetics, showing few records of rapid displays and behavior signals. Here, we present a novel multidisciplinary approach capable of advancing research on reptiles' behavior. Our proposed approach uses advanced technologies for the digitization, reconstruction and visualization of reptiles and their behavior. We (i) record movement through tri-axial accelerometers, video cameras, and motion capture systems; (ii) ground-truth data through the video records; (iii) develop realistically accurate 3D avatars of the recorded movement for visualization purposes, and (iv) archive data on a Behavior Pattern Database. As case studies, we used two small Mediterranean reptiles, the lizard Laudakia cypriaca and the snake Dolichophis jugularis. Through our approach, we successfully recorded, ground-truthed, and labeled for the first time, several detailed movements and behaviors of the two case study species. We developed an accurate digital overview of those movements using motion capture and 3D animal reconstruction. Finally, we structured a database for archiving all behavioral data and demonstrated how those archives can be used for advancing behavioral research, providing ecological insights into this animal group. Our approach can enhance research on reptiles' behavior by contributing to the analysis of complex or isolated behaviors, poorly studied, such as signals and social interactions, providing valuable insights and assisting behavioral analysis.

Animals navigating in fluid environments often face forces from wind or water currents that challenge travel efficiency and route accuracy. We investigated how 27 Magellanic penguins (Spheniscus magellanicus) adapt their navigation strategies to return to their colony amid regional tidal ocean currents. Using GPS-enhanced dead-reckoning loggers and high-resolution ocean current data, we reconstructed penguin travel vectors during foraging trips to assess their responses to variable currents during their colony-bound movements. By integrating estimates of energy costs and prey pursuits, we found that birds balanced direct navigation with current-driven drift: in calm currents, they maintained precise line-of-sight headings to their colony. In stronger currents, they aligned their return with lateral flows, which increased travel distance, but at reduced energy costs, and provided them with increased foraging opportunities. Since the lateral tidal currents always reversed direction over the course of return paths, the penguins' return paths were consistently S-shaped but still resulted in the birds returning efficiently to their colonies. These findings suggest that Magellanic penguins can sense current drift and use it to enhance energy efficiency by maintaining overall directional accuracy while capitalizing on foraging opportunities.

Travel is considered to account for a substantial proportion of endothermic species energy expenditure. However, transport costs depend on speed of the animal and slope angle of the terrain. We used biologging data from six ungulate species within the French mountains, combined with mapping data, to examine how these different species reacted to slopes by varying travel speed, and chosen ascent and descent angles, in relation to vectoral dynamic body acceleration (VeDBA; as a proxy for energy expenditure). As predicted by theory and as seen in pumas, animals travelled obliquely so that the angle that any individual experienced was lower than that of the topography. Travel speed affected the VeDBA-based proxy for cost of transport (COT) even though most species moved slower on steeper inclines. Models that considered speed, COT, slope, and habitat type showed clear relationships between COT and slope with variation across habitat types and according to species. Species-specific choice of travel speeds and slope chosen by animals underpins fundamental differences in species physiology and ecology via links in heat production and time spent per altitude. Understanding these interrelations points to the complexity of factors affecting space use by mountain ungulates and is crucial for conservation efforts, especially in fast-changing environments where energy expenditure, temperature changes, and resource accessibility impact population wellbeing.

Animal movement paths display substantial complexity and variability, leading researchers to seek underlying rules that govern these patterns and mathematical models that best describe them. Using high-resolution (≥ 10 Hz) movement from 43 vertebrate species across diverse taxa, mass, and lifestyles, we show that movement paths are universally composed of straight-line steps interspersed with sharp turns, echoing a pattern documented for lower taxa such as bacteria. We report how these vertebrate ‘fundamental step lengths’ and ‘fundamental turn angles’, which are intrinsically different from the straight-line paths detailed in studies using low resolution position data, vary with species’ mass, lifestyle, behaviour, and environmental context. To explain these, we posit that animals inherently move in a straight line until sensory information signals a perceived better heading, which instigates a turn. The constellation of fundamental step lengths and turn angles over varying time intervals affects how well different models of animal movement (such as random walk or Lévy flight) fit lower resolution data. By examining turns as decision points, we can seek drivers of animal movement patterns and thereby work to predict future paths under varying conditions.

Animals navigating in fluid environments often face lateral forces from wind or water currents that challenge travel efficiency and route accuracy. We investigated how 27 Magellanic penguins (Spheniscus magellanicus) adapt their navigation strategies to return to their colony amid regional tidal ocean currents. Using GPS-enhanced dead-reckoning loggers and high-resolution ocean current data, we reconstructed penguin travel vectors during foraging trips to assess their responses to variable currents during their colony-bound movements. By integrating estimates of energy costs and prey pursuits, we found that birds balanced direct navigation with current-driven drift: in calm currents, they maintained precise line-of-sight headings to their colony. In stronger currents, they aligned their return with lateral flows, which increased travel distance, but at minimal energy costs, and provided them with increased foraging opportunities. Since the lateral tidal currents always reversed direction over the course of return paths, the penguins’ return paths were consistently S-shaped but still resulted in the birds returning efficiently to their colonies. These findings suggest that Magellanic penguins can sense current drift and use it to optimize energy expenditure by maintaining overall directional accuracy while capitalizing on foraging opportunities. ### Competing Interest Statement The authors have declared no competing interest.

Many animals return to their home areas (i.e., ‘homing’) after translocation to sites further away. Such translocations have traditionally been used in behavioral ecology to understand the orientation and migration behavior of animals. The movement itself can then be followed by marking and recapturing animals or by tracking, for example, using GPS systems. Most detailed studies investigating this behavior have been conducted in smaller vertebrates (e.g., birds, amphibians, and mice), whereas information on larger mammals, such as red deer, is sparse. We conducted GPS-assisted translocation experiments with red deer at two sites in the Czech Republic. Individuals were translocated over a distance of approximately 11 km and their home journey was tracked. Circular statistics were used to test for significant homeward orientation at distances of 100, 500, 1000, and 5000 m from the release site. In addition, we applied Lavielle trajectory segmentation to identify the different phases of homing behavior. Thirty-one out of 35 translocations resulted in successful homing, with a median time of 4.75 days (range 1.23–100 days). Animals were significantly oriented towards home immediately after release and again when they came closer to home; however, they did not show a significant orientation at the distances in between. We were able to identify three homing phases, an initial ‘exploratory phase’, followed by a ‘homing phase’ which sometimes was again followed by an ‘arrival phase’. The ‘homing phase’ was characterized by the straightest paths and fastest movements. However, the variation between translocation events was considerable. We showed good homing abilities of red deer after translocation. Our results demonstrate the feasibility of conducting experiments with environmental manipulations (e.g., to impede the use of sensory cues) close to the release site. The homing behavior of red deer is comparable to that of other species, and might represent general homing behavior patterns in animals. Follow-up studies should further dissect and investigate the drivers of the individual variations observed and try to identify the sensory cues used during homing.

AbstractBiologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal‐borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom‐designed integrated multisensor collar (IMSC), which was field tested on 71 free‐ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free‐roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field‐based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals.

Wildlife tourism can benefit conservation of target species, however, it can have detrimental effects on animal behaviour and physiology. Whale shark Rhincodon typus tourism has seen recent rapid growth globally, but methods and regulations vary widely. Ningaloo Reef, Australia is considered "gold standard" whale shark tourism management due to legal regulation, strict enforcement, and high compliance. Rather than relying on observational data, we used biotelemetry to collect high-resolution data (20 Hz) on whale sharks' movement behaviour in the presence or absence of tourists. Tourism encounters lasted an average of 62 min and swimming with tourists increased the activity levels of larger (> 7 m) but not smaller sharks. Given that activity levels positively correlate with energetic costs, it is likely the 18% increase seen in activity of large sharks would have incurred additional energetic costs. However, when considered as a proportion of daily energy requirements, these additional costs were only incurred for an average of 4% of a whale shark's day. The tourism-induced impacts we found on the endangered whale sharks at this highly regulated tourism site would not have been apparent from purely observational studies, highlighting the utility of biotelemetry to quantify tourism-related impacts on wildlife.

Abstract Background: The attachment of electronic tags to animals has led to data collection that has hugely enhanced our understanding of wild animal behavioural ecology and physiology. However, animals are normally captured and restrained/sedated so that the tags can be attached, which is stressful for the animals and threatens to compromise the quality of the data gathered, at least during an initial acclimation period. We note that many plant seeds have evolved to become attached to passing animals and suggest that an approach, based on plant burs, could be used to attach tags to animals without capture or restraint. Methods: We present a framework for ‘bur-tagging’ and provide details of the design of a bur-tagging system, highlighting issues that we feel should be considered for the approach to be successful. Results: We report how the tagging site in the environment and animal neophobia critically affect the probability that an animal will be tagged over any given time period and also document what needs to be done to ensure that only the target species is tagged as well as illustrating the steps that can be taken to enhance the accuracy of tag placement on the animal. In addition, we discuss the criticality of the choice of the adhesive mechanism between the tag and the animal and illustrate how animals react to being tagged using this system. Conclusion: Although in an early stage of development, we believe that ‘bur-tagging’ shows great promise for deploying sophisticated electronic tags on wild animals with less stress than the conventional capture and restraint approach.

Zoos are valuable resources for research, providing scientists with access to rare and elusive species in an easy to observe environment. Animal-attached loggers (aka biologgers) offer profound insight into animal behaviour. Their use in zoos has high yet largely untapped potential to collect data relevant for wild animal research and conservation but also welfare and enrichment monitoring of the zoo animals themselves. However, affixing biologgers to study animals can be problematic in captive settings, limiting the accessibility of this technology for use on zoo species which ordinarily need to be sedated for the fitting of such devices, including large carnivores. Here we show that biologging collars and crate-training allow collection of novel datasets on captive animals with high welfare and conservation value, using endangered African wild dogs ( Lycaon pictus ) tagged with tri-axial accelerometer and tri-axial magnetometer loggers, as a case study. Two yearling female wild dogs were fitted with biologging collars while sedated in preparation for translocation from London to Whipsnade Zoo, with data collected for 10–26 hours until collar detachment. Two adult male wild dogs at London Zoo were trained to accept collars in a modified crate in exchange for a food reward, which allowed fitting and detaching the collars without sedation, with data collected for 28 days. First, we show how accelerometer and magnetometer data allow detection of fine-scale individual differences in the recovery from sedation as well as within- and between-individual variation in activity patterns in relation to the type of food received (tong vs. rabbit and pony carcass). Using the vectorial dynamic body acceleration metric (VeDBA), a proxy for movement-related energy expenditure, further shows that daily energy expenditure was higher on days with partial pony carcass feeds compared to rabbit feeds but varied considerably between days where flesh pieces were fed with tongs. Using the dead-reckoning method allowed reconstruction of fine-scale (1 Hz locations) movement paths within enclosures, indoors and outdoors, allowing visualisation and quantification of fine-scale movement and space use differences between individuals and over time, for example in response to different enrichment methods. Using multi-sensor biologgers, combined with training captive animals to accept collars without the use of anaesthetic, can enable flexible, experimental approaches to data collection with minimal impact on study animals, providing novel understanding of relevance for both zoo and wild animals.

Understanding the processes that determine how animals allocate time to space is a major challenge, although it is acknowledged that summed animal movement pathways over time must define space-time use. The critical question is then, what processes structure these pathways? Following the idea that turns within pathways might be based on environmentally determined decisions, we equipped Arabian oryx with head- and body-mounted tags to determine how they orientated their heads – which we posit is indicative of them assessing the environment – in relation to their movement paths, to investigate the role of environment scanning in path tortuosity. After simulating predators to verify that oryx look directly at objects of interest, we recorded that, during routine movement, > 60% of all turns in the animals’ paths, before being executed, were preceded by a change in head heading that was not immediately mirrored by the body heading: The path turn angle (as indicated by the body heading) correlated with a prior change in head heading (with head heading being mirrored by subsequent turns in the path) twenty-one times more than when path turns occurred due to the animals adopting a body heading that went in the opposite direction to the change in head heading. Although we could not determine what the objects of interest were, and therefore the proposed reasons for turning, we suggest that this reflects the use of cephalic senses to detect advantageous environmental features (e.g. food) or to detect detrimental features (e.g. predators). The results of our pilot study suggest how turns might emerge in animal pathways and we propose that examination of points of inflection in highly resolved animal paths could represent decisions in landscapes and their examination could enhance our understanding of how animal pathways are structured.

Human societies depend on marine ecosystems, but their degradation continues. Toward mitigating this decline, new and more effective ways to precisely measure the status and condition of marine environments are needed alongside existing rebuilding strategies. Here, we provide an overview of how sensors and wearable technology developed for humans could be adapted to improve marine monitoring. We describe barriers that have slowed the transition of this technology from land to sea, update on the developments in sensors to advance ocean observation and advocate for more widespread use of wearables on marine organisms in the wild and in aquaculture. We propose that large-scale use of wearables could facilitate the concept of an ‘internet of marine life’ that might contribute to a more robust and effective observation system for the oceans and commercial aquaculture operations. These observations may aid in rationalizing strategies toward conservation and restoration of marine communities and habitats.

Abstract Understanding the behavioral ecology of wildlife that experiences negative interactions with humans and the outcome of any wildlife management intervention is essential. In the Cape Peninsula, South Africa, chacma baboons (Papio ursinus) search for anthropogenic food sources in both urban and agricultural areas. In response, the city of Cape Town and private farmers employ “rangers” to keep baboons within the Table Mountain National Park. In this study, we investigated the success of rangers' intervention in keeping baboons in their natural habitat. Based on our findings in year one, we recommended adjustments to the rangers' management strategy in year two. We recommended improved consensus of actions toward baboons (that is, when/where to herd them), and the construction of a baboon‐proof fence around one of the farms that provided a corridor to urban areas. During the 2 months following recommendations, these interventions combined resulted in a significant reduction in the time baboons spent in both urban and agricultural land. Our case study illustrates the importance of integrating research findings into ongoing management actions to improve both human livelihoods and baboon conservation through an adaptive management framework. We expect similar approaches to be beneficial in a wide range of species and contexts.

Quantification of activity budgets is pivotal for understanding how animals respond to changes in their environment. Social grooming is a key activity that underpins various social processes with consequences for health and fitness. Traditional methods use direct (focal) observations to calculate grooming rates, providing systematic but sparse data. Accelerometers, in contrast, can quantify activity budgets continuously but have not been used to quantify social grooming. We test whether grooming can be accurately identified using machine learning (random forest model) trained on labelled acceleration data from wild chacma baboons (Papio ursinus). We successfully identified giving and receiving grooming with high precision (81% and 91%) and recall (87% and 79%). Giving grooming was associated with a distinct rhythmical signal along the surge axis. Receiving grooming had similar acceleration signals to resting, and thus was more difficult to assign. We applied our machine learning model to n = 680 collar data days from n = 12 baboons and found that grooming rates obtained from accelerometers were significantly and positively correlated with direct observation rates for giving but not receiving grooming. The ability to collect continuous grooming data in wild populations will allow researchers to re-examine and expand upon long-standing questions regarding the formation and function of grooming bonds.

Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence. Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to identify the unique signal characteristics associated with each behavior. In contrast, unsupervised approaches aggregate unlabeled signals into groups based purely on signal similarity but, without additional information, do not identify specific behaviors. In this paper, we propose a probabilistic framework for interpreting uncertainty in machine learning techniques-the probability profile-and demonstrate how to post hoc identify behaviors within signal groups. We assess model performance using a matrix-based measure of dissimilarity. We used a Random Forest (RF) and a clustered self-organizing map (CSOM) for comparison and demonstrate the use of a behavioral profile for each using a data set of high-frequency accelerometer and magnetometer data collected from 7 captive wild pigs (Sus scrofa) moving in a 1 ha outdoor enclosure. We found that the RF had more discrimination than the CSOM which had fewer clusters associated with high probabilities of a single behavior (>50%). The leave-p-out cross validation statistic of the probability matrix (<(L-1)over bar>) indicated that there was an average maximum dissimilarity of 20% and 65% between the training and test data sets for the RF and CSOM methods, respectively. Using a probability profile to describe groups predicted from machine learning allows the variation and error inherent in behavioral prediction to be incorporated directly into the model to better reflect the nuances of behavior derived from accelerometer and/or magnetometer signals. We discuss the data requirements of this framework, demonstrate its application to field data, highlight critical assumptions and caveats, and examine how it may be used to generate new ecological inference.

Accelerometers in animal-attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement-based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimisation.Using laboratory trials, we examine the absolute accuracy of tri-axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back- and tail-mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red-tailed tropicbirds Phaethon rubricauda foraging in different seasons.Bench tests showed that individual acceleration axes required a two-level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper and lower back-mounted tags varying by 9% in pigeons, and tail- and back-mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons.Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

It has been proposed that predators searching for prey acquire food according to a probabilistic framework, where success is based on ‘luck’ and the odds of success vary with prey abundance. If true, this has major ramifications for variation in the rates of energy acquisition within animal populations, which is particularly pertinent in offspring provisioning and breeding success, because smaller animals (the young) cannot starve for as long as the adults. However, despite much general speculation about rates of food acquisition, no study has measured whether food encounter is probabilistic in wild animals. We used animal-mounted cameras to document all prey captures by wild imperial shagsLeucocarbo atricepsas they hunted underwater and show that, although they mostly do not have inter-prey acquisition time distributions that accord with a ‘luck-based’ framework assuming a constant probability of finding prey over time, there is no difference in the predicted amount of food captured between models that use the empirical data or theoretical Poisson-based fits of the data. We also noted considerable inter-individual differences in foraging success that far exceeded any differences between empirical and theoretical inter-prey acquisition time distributions. The data were used in a probabilistic foraging model that made explicit the mechanistic link between random prey encounters and food-dependent breeding success, indicating that ‘less lucky’ individuals could not provision their broods at rates commensurate with normal growth while the ‘lucky’ birds could do so easily. Given the nature of food encounter in these birds, coupled with substantial inter-individual variation in foraging success, we suggest that more successful individuals are particularly choosey about when, how and where to forage, which results in them operating with higher odds of success.

Travel represents a major cost for many animals so there should be selection pressure for it to be efficient - at minimum cost. However, animals sometimes exceed minimum travel costs for reasons that must be correspondingly important. We use Dynamic Body Acceleration (DBA), an acceleration-based metric, as a proxy for movement-based power, in tandem with vertical velocity (rate of change in depth) in a shark (Rhincodon typus) to derive the minimum estimated power required to swim at defined vertical velocities. We show how subtraction of measured DBA from the estimated minimum power for any given vertical velocity provides a "proxy for power above minimum" metric (PPAmin), highlighting when these animals travel above minimum power. We suggest that the adoption of this metric across species has value in identifying where and when animals are subject to compelling conditions that lead them to deviate from ostensibly judicious energy expenditure.

The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions (Panthera leo) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.