Precise monitoring of porpoising behaviour of Adelie penguins determined using acceleration data loggers

Biologging and Biotelemetry: Tools for Understanding the Lives and Environments of Marine Animals

Addressing important questions in animal ecology, physiology, and environmental science often requires in situ information from wild animals. This difficulty is being overcome by biologging and biotelemetry, or the use of miniaturized animal-borne sensors. Although early studies recorded only simple parameters of animal movement, advanced devices and analytical methods can now provide rich information on individual and group behavior, internal states, and the surrounding environment of free-ranging animals, especially those in marine systems. We summarize the history of technologies used to track marine animals. We then identify seven major research categories of marine biologging and biotelemetry and explain significant achievements, as well as future opportunities. Big data approaches via international collaborations will be key to tackling global environmental issues (e.g., climate change impacts), and curiosity about the secret lives of marine animals will also remain a major driver of biologging and biotelemetry studies.

The science and technology of kinematic measurements in a century of Journal of Experimental Biology

Kinematic measurements have been essential to the study of comparative biomechanics and offer insight into relationships between technological development and scientific progress. Here, we review the 100 year history of kinematic measurements in Journal of Experimental Biology (JEB) through eras that used film, analog video and digital video, and approaches that have circumvented the use of image capture. This history originated with the career of Sir James Gray and has since evolved over the generations of investigators that have followed. Although some JEB studies have featured technological developments that were ahead of their time, the vast majority of research adopted equipment that was broadly available through the consumer or industrial markets. We found that across eras, an emphasis on high-speed phenomena outpaced the growth of the number of articles published by JEB and the size of datasets increased significantly. Despite these advances, the number of species studied within individual reports has not differed significantly over time. Therefore, we find that advances in technology have helped to enable a growth in the number of JEB studies that have included kinematic measurements, contributed to an emphasis on high-speed phenomena, and yielded biomechanical studies that are more data rich, but are no more comparative now than in previous decades.

Developing a classification system to assign activity states to two species of freshwater turtles

Research in ecology often requires robust assessment of animal behaviour, but classifying behavioural patterns in free-ranging animals and in natural environments can be especially challenging. New miniaturised bio-logging devices such as accelerometers are increasingly available to record animal behaviour remotely, and thereby address the gap in knowledge related to behaviour of free-ranging animals. However, validation of these data is rarely conducted and classification model transferability across closely-related species is often not tested. Here, we validated accelerometer and water sensor data to classify activity states in two free-ranging freshwater turtle species (Blanding's turtle, Emydoidea blandingii, and Painted turtle, Chrysemys picta). First, using only accelerometer data, we developed a decision tree to separate motion from motionless states, and second, we included water sensor data to classify the animal as being motionless or in-motion on land or in water. We found that accelerometers separated in-motion from motionless behaviour with > 83% accuracy, whereas models also including water sensor data predicted states in terrestrial and aquatic locations with > 77% accuracy. Despite differences in values separating activity states between the two species, we found high model transferability allowing cross-species application of classification models. Note that reducing sampling frequency did not affect predictive accuracy of our models up to a sampling frequency of 0.0625 Hz. We conclude that the use of accelerometers in animal research is promising, but requires prior data validation and development of robust classification models, and whenever possible cross-species assessment should be conducted to establish model generalisability.

An implantable neurophysiology platform: Broadening research capabilities in free-living and non-traditional animals

Animal-borne sensors that can record and transmit data (“biologgers”) are becoming smaller and more capable at a rapid pace. Biologgers have provided enormous insight into the covert lives of many free-ranging animals by characterizing behavioral motifs, estimating energy expenditure, and tracking movement over vast distances, thereby serving both scientific and conservational endpoints. However, given that biologgers are usually attached externally, access to the brain and neurophysiological data has been largely unexplored outside of the laboratory, limiting our understanding of how the brain adapts to, interacts with, or addresses challenges of the natural world. For example, there are only a handful of studies in free-living animals examining the role of sleep, resulting in a wake-centric view of behavior despite the fact that sleep often encompasses a large portion of an animal’s day and plays a vital role in maintaining homeostasis. The growing need to understand sleep from a mechanistic viewpoint and probe its function led us to design an implantable neurophysiology platform that can record brain activity and inertial data, while utilizing a wireless link to enable a suite of forward-looking capabilities. Here, we describe our design approach and demonstrate our device’s capability in a standard laboratory rat as well as a captive fox squirrel. We also discuss the methodological and ethical implications of deploying this new class of device “into the wild” to fill outstanding knowledge gaps.

Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras

Direct observation of foraging behavior is not always possible, especially for marine species that hunt underwater. However, biologging and tracking devices have provided detailed information about how various species use their habitat. From these indirect observations, researchers have inferred behaviors to address a variety of research questions, including the definition of ecological niches. In this study, we deployed video cameras with GPS and time-depth recorders on 16 chinstrap penguins (Pygoscelis antarcticus) during the brood phase of the 2018–2019 breeding season on Signy (South Orkney Islands). More than 57 h of footage covering 770 dives were scrutinized by two observers. The outcome of each dive was classified as either no krill encounter, individual krill or krill swarm encounter and the number of prey items caught per dive was estimated. Other variables derived from the logging devices or from the environment were used to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable. We also demonstrate that it was possible to accurately predict the outcome of each dive from dive and horizontal movement variables in a manner that has not been used for penguins previously. For example, our models show that a fast dive ascent rate and a high density of dives are good indicators of krill and especially of swarm encounter. Finally, we discuss how video footage can help build accurate habitat models to provide wider knowledge about predator behavior or prey distribution.

Ecological inference using data from accelerometers needs careful protocols

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.

The role of California sea lion (Zalophus californianus) hindflippers as aquatic control surfaces for maneuverability

California sea lions (Zalophus californianus) are a highly maneuverable species of marine mammal. During uninterrupted, rectilinear swimming, sea lions oscillate their foreflippers to propel themselves forward without aid from the collapsed hindflippers, which are passively trailed. During maneuvers such as turning and leaping (porpoising), the hindflippers are spread into a delta-wing configuration. There is little information defining the role of otarrid hindflippers as aquatic control surfaces. To examine Z. californianus hindflippers during maneuvering, trained sea lions were video recorded underwater through viewing windows performing porpoising behaviors and banking turns. Porpoising by a trained sea lion was compared with sea lions executing the maneuver in the wild. Anatomical points of reference (ankle and hindflipper tip) were digitized from videos to analyze various performance metrics and define the use of the hindflippers. During a porpoising bout, the hindflippers were considered to generate lift when surfacing with a mean angle of attack of 14.6±6.3 deg. However, while performing banked 180 deg turns, the mean angle of attack of the hindflippers was 28.3±7.3 deg, and greater by another 8-12 deg for the maximum 20% of cases. The delta-wing morphology of the hindflippers may be advantageous at high angles of attack to prevent stalling during high-performance maneuvers. Lift generated by the delta-shaped hindflippers, in concert with their position far from the center of gravity, would make these appendages effective aquatic control surfaces for executing rapid turning maneuvers.

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)'.

The Effects of Live Feeding on Swimming Activity and Exhibit Use in Zoo Humboldt Penguins (Spheniscus humboldti)

Penguins are considered among the most popular animals for zoo and aquarium visitors to observe. Swimming is considered a desirable activity, both for the visitor experience and the welfare of the penguins. However, little is known about the amount of time exhibited penguins spend swimming, or how such swimming is related to regular feeding events. We examined the effects of introducing live prey in the form of trout on 22 Humboldt penguins living at the Woodland Park Zoo. Of primary interest was how the live feeds changed (1) daily and hourly swimming activity, and (2) variability in enclosure use. We hypothesized that the live feedings would increase swimming activity prior to and during the delivery of the live trout, as well as create an overall increase in total swimming activity for live feed days compared to non-live feed days. We also predicted that the penguins would be more likely to use the entire exhibit around these live feeds, since they are likely to chase fish throughout the exhibit. Penguins did show an increase in swimming activity in the hour prior to and during the live feed, with a small decrease in swimming activity following the live feed when compared to non-live feed days. There was also a more than 30% increase in the total swimming activity for live feed days when compared to all other non-live feed days. In addition, a single measure of variability in enclosure use (entropy) showed greater overall enclosure use for the live feed days compared to the non-live feed days. These results demonstrate that live fish can be a useful way of enriching the behavioural welfare of Humboldt penguins.

Fecal Glucocorticoid Metabolite Concentration as a Tool for Assessing Impacts of Interventions in Humboldt Penguins (Spheniscus humboldti)

It is currently unknown if current guidelines for collecting and interpreting blood corticosterone in flying birds can be extrapolated to penguins. It is also difficult to collect blood quickly without causing stress to a penguin. Therefore, immunoreactive fecal glucocorticoid metabolites (FGCMs) may be the most practical and minimally invasive way of monitoring the stress levels of penguins. This study investigated the reliability of FGCMs for monitoring stress levels in captive Humboldt Penguins (Spheniscus humboldti) at Jurong Bird Park, Singapore. Humboldt Penguin feces were randomly sampled and pooled from the exhibit for 2 months. The penguins were restrained and transported on three separate occasions to simulate stressful events. The feces were analyzed using an enzyme immunoassay to measure corticosterone levels. There were significant increases lasting 3 to 7 days in the FGCM levels after a stressful event. This method was then used to test whether accelerometer vests used for behavior quantification caused stress responses in these birds. There was no significant difference in FGCMs between time periods with and without the accelerometer vests. The results indicated that FGCMs can be an accurate indication of capture-, restraint-, and transport-related stress in Humboldt Penguins, and that accelerometer vests do not appear to cause stress.

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology

The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

Training penguins to interact with enrichment devices for lasting effects

The modern zoo has brought about two major advances in the behavioral welfare of their exhibited animals: (a) The use of environmental enrichment to promote naturalistic behaviors and (b) the use of training to improve voluntary husbandry care. Whereas training itself has been talked about as an effective enrichment strategy, little has been done to combine training procedures with enrichment. Typically, enrichment is treated as a trial and error process, where potential enrichment items or procedures are cycled through until successful enrichment is found. The use of shaping or other training techniques has seldom been documented to increase engagement with possible enrichment items or procedures. The following study examined the possibility of combining training and enrichment to produce continued interactions with enrichment devices. Two species of penguin, Magellanic and southern rockhopper penguins, were studied. Two measures were taken: Time spent swimming and contact with enrichment devices. The enrichment devices could be manipulated by placing fish within and hanging out of each device. During baseline sessions, no hits to either device were observed. During training sessions, several hits were recorded when fish were in the devices and overall swimming time increased during these conditions. When baseline was reintroduced without fish in the devices, contact with the enrichment devices rapidly declined and swimming time for the rockhopper penguins decreased. When the devices were reintroduced with fish but without training, the greatest number of enrichment device contacts and the highest percentage of time spent swimming were observed for the rockhopper penguins.

Shorebird feeding specialists differ in how environmental conditions alter their foraging time

Feeding specialization is a common cause of individual variation. Fitness payoffs of specialization vary with environmental conditions, but the underlying behavioral mechanisms are poorly understood. Such mechanistic knowledge, however, is crucial to reliably predict responses of heterogeneous populations to environmental change. We quantified spatiotemporal allocation of foraging behavior in wintering Eurasian oystercatchers (Haematopus ostralegus), a species in which feeding specialization can be inferred from bill shape. We combined global positioning system (GPS) and accelerometer data to quantify foraging time of 64 individuals for every tidal period in one or two winter seasons. Individuals varied widely in foraging time (3.7–6.5 h per tidal period) and individuals that spend more time foraging had lower inferred survival. Feeding specialization appeared a major determinant of individual variation in foraging time and its spatiotemporal allocation. Visually hunting worm specialists foraged more during day time and complemented intertidal foraging with grassland foraging when the exposure of intertidal flats was limited and nights were well illuminated. Shellfish specialists increased total foraging time in cold weather, whereas foraging time of worm specialists decreased as frosty grasslands became inaccessible. Our results imply that worm specialists may be most sensitive to cold snaps and daytime disturbance, whereas shellfish specialists are most sensitive to high water levels. These behavioral responses can be implemented in population models to predict the vulnerability of heterogeneous populations to environmental change and, thereby, provide a shortcut to long-term population studies that require fitness data across many years and conditions to make similar projections.

An Injectable System for Subcutaneous Photoplethysmography, Accelerometry, and Thermometry in Animals

Obtaining physiological data from animals in a non-obtrusive and continuous manner is important to veterinary science. This paper demonstrates the design and deployment of a miniaturized capsule-based system for subdermal injection to provide real-time and continuous heart-rate, movement, and core-body-temperature measurements. The presented device incorporates sensors for photoplethysmography, motion detection, and temperature measurements. A bluetooth-low-energy enabled microcontroller configures the sensors, digitizes the sensor information, and wirelessly connects with external devices. The device is powered by a CR425 battery for this paper, and various other battery solutions are available based upon the use case. The design uses only commercially available integrated circuits in order to reduce the development cost and be modular. The encapsulation is a combination of medical epoxy and poly(methyl methacrylate) that fits within a 6-gauge hypodermic needle. The preliminary evaluation of the device included an in vitro assessment of its thermal response and measurement accuracy, the impact of one-month implantation on surrounding tissue, the power consumption with duty cycling of various sensors, and a measurement of physiological signals in a rat and a chicken. Having a form factor and implantation method similar to existing devices for animals, this novel system is a useful platform for both scientists and veterinarians to better study a diverse range of animals.

Optimizing the use of biologgers for movement ecology research

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.

Horsing Around—A Dataset Comprising Horse Movement

Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.

Behavioral classification of low-frequency acceleration and temperature data from a free-ranging small mammal

The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal-borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under-acknowledged consideration in biologging is the trade-off between sampling rate and sampling duration, created by battery- (or memory-) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low-frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free-ranging red squirrels (200-300 g) that were fitted with accelerometers (2 g) recording tri-axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long-duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi-month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.

High accuracy at low frequency: detailed behavioural classification from accelerometer data

Accelerometers are a valuable tool for studying animal behaviour and physiology where direct observation is unfeasible. However, giving biological meaning to multivariate acceleration data is challenging. Here, we describe a method that reliably classifies a large number of behaviours using tri-axial accelerometer data collected at the low sampling frequency of 1 Hz, using the dingo (Canis dingo) as an example. We used out-of-sample validation to compare the predictive performance of four commonly used classification models (random forest, k-nearest neighbour, support vector machine, and naïve Bayes). We tested the importance of predictor variable selection and moving window size for the classification of each behaviour and overall model performance. Random forests produced the highest out-of-sample classification accuracy, with our best-performing model predicting 14 behaviours with a mean accuracy of 87%. We also investigated the relationship between overall dynamic body acceleration (ODBA) and the activity level of each behaviour, given the increasing use of ODBA in ecophysiology as a proxy for energy expenditure. ODBA values for our four 'high activity' behaviours were significantly greater than all other behaviours, with an overall positive trend between ODBA and intensity of movement. We show that a random forest model of relatively low complexity can mitigate some major challenges associated with establishing meaningful ecological conclusions from acceleration data. Our approach has broad applicability to free-ranging terrestrial quadrupeds of comparable size. Our use of a low sampling frequency shows potential for deploying accelerometers over extended time periods, enabling the capture of invaluable behavioural and physiological data across different ontogenies.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Accelerometers are becoming ever more important sensors in animal-attached technology, providing data that allow determination of body posture and movement and thereby helping to elucidate behaviour in animals that are difficult to observe. We sought to validate the identification of sea turtle behaviours from accelerometer signals by deploying tags on the carapace of a juvenile loggerhead (Caretta caretta), an adult hawksbill (Eretmochelys imbricata) and an adult green turtle (Chelonia mydas) at Aquarium La Rochelle, France. We recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. We identified behaviours from the acceleration data using two different supervised learning algorithms, Random Forest and Classification And Regression Tree (CART), treating the data from the adult animals as separate from the juvenile data. We achieved a global accuracy of 81.30% for the adult hawksbill and green turtle CART model and 71.63% for the juvenile loggerhead, identifying 10 and 12 different behaviours, respectively. Equivalent figures were 86.96% for the adult hawksbill and green turtle Random Forest model and 79.49% for the juvenile loggerhead, for the same behaviours. The use of Random Forest combined with CART algorithms allowed us to understand the decision rules implicated in behaviour discrimination, and thus remove or group together some 'confused' or under--represented behaviours in order to get the most accurate models. This study is the first to validate accelerometer data to identify turtle behaviours and the approach can now be tested on other captive sea turtle species.

A miniaturized threshold-triggered acceleration data-logger for recording burst movements of aquatic animals

Although animal-borne accelerometers are effective tools for quantifying the kinematics of animal behaviors, quantifying the burst movements of small and agile aquatic animals remains challenging. To capture the details of burst movements, accelerometers need to sample at a very high frequency, which will inevitably shorten the recording duration or increase the device size. To overcome this problem, we developed a high-frequency acceleration data-logger that can be triggered by a manually defined acceleration threshold, thus allowing the selective measurement of burst movements. We conducted experiments under laboratory and field conditions to examine the performance of the logger. The laboratory experiment using red seabream (Pagrus major) showed that the new logger could measure the kinematics of their escape behaviors. The field experiment using free-swimming yellowtail kingfish (Seriola lalandi) showed that the loggers trigger correctly. We suggest that this new logger can be applied to measure the burst movements of various small and agile animals.

Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data

Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44'N, 79°16'W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations.

Combined use of tri-axial accelerometers and GPS reveals the flexible foraging strategy of a bird in relation to weather conditions

Tri-axial accelerometry has proved to be a useful technique to study animal behavior with little direct observation, and also an effective way to measure energy expenditure, allowing a refreshing revisit to optimal foraging theory. This theory predicts that individuals should gain the most energy for the lowest cost in terms of time and energy when foraging, in order to maximize their fitness. However, during a foraging trip, central-place foragers could face different trade-offs during the commuting and searching parts of the trip, influencing behavioral decisions. Using the lesser kestrel (Falco naumanni) as an example we study the time and energy costs of different behaviors during the commuting and searching parts of a foraging trip. Lesser kestrels are small insectivorous falcons that behave as central-place foragers during the breeding season. They can commute by adopting either time-saving flapping flights or energy-saving soaring-gliding flights, and capture prey by using either time-saving active hovering flights or energy-saving perch-hunting. We tracked 6 lesser kestrels using GPS and tri-axial accelerometers during the breeding season. Our results indicate that males devoted more time and energy to flight behaviors than females in agreement with being the sex responsible for food provisioning to the nest. During the commuting flights, kestrels replaced flapping with soaring-gliding flights as solar radiation increased and thermal updrafts got stronger. In the searching part, they replaced perch-hunting with hovering as wind speed increased and they experienced a stronger lift. But also, they increased the use of hovering as air temperature increased, which has a positive influence on the activity level of the preferred prey (large grasshoppers). Kestrels maintained a constant energy expenditure per foraging trip, although flight and hunting strategies changed dramatically with weather conditions, suggesting a fixed energy budget per trip to which they adjusted their commuting and searching strategies in response to weather conditions.

Southern Elephant Seals Replenish Their Lipid Reserves at Different Rates According to Foraging Habitat

Assessing energy gain and expenditure in free ranging marine predators is difficult. However, such measurements are critical if we are to understand how variation in foraging efficiency, and in turn individual body condition, is impacted by environmentally driven changes in prey abundance and/or accessibility. To investigate the influence of oceanographic habitat type on foraging efficiency, ten post-breeding female southern elephant seals Mirounga leonina (SES) were equipped and tracked with bio-loggers to give continuous information of prey catch attempts, body density and body activity. Variations in these indices of foraging efficiency were then compared between three different oceanographic habitats, delineated by the main frontal structures of the Southern Ocean. Results show that changes in body density are related not only to the number of previous prey catch attempts and to the body activity (at a 6 day lag), but also foraging habitat type. For example, despite a lower daily prey catch attempt rate, SESs foraging north of the sub-Antarctic front improve their body density at a higher rate than individuals foraging south of the sub-Antarctic and polar fronts, suggesting that they may forage on easier to catch and/or more energetically rich prey in this area. Our study highlights a need to understand the influence of habitat type on top predator foraging behaviour and efficiency when attempting a better comprehension of marine ecosystems.

Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales

The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.

Effect of walking speed on the gait of king penguins: An accelerometric approach

Little is known about non-human bipedal gaits. This is probably due to the fact that most large animals are quadrupedal and that non-human bipedal animals are mostly birds, whose primary form of locomotion is flight. Very little research has been conducted on penguin pedestrian locomotion with the focus instead on their associated high energy expenditure. In animals, tri-axial accelerometers are frequently used to estimate physiological energy cost, as well as to define the behaviour pattern of a species, or the kinematics of swimming. In this study, we showed how an accelerometer-based technique could be used to determine the biomechanical characteristics of pedestrian locomotion. Eight king penguins, which represent the only family of birds to have an upright bipedal gait, were trained to walk on a treadmill. The trunk tri-axial accelerations were recorded while the bird was walking at four different speeds (1.0, 1.2, 1.4 and 1.6km/h), enabling the amplitude of dynamic body acceleration along the three axes (amplitude of DBAx, DBAy and DBAz), stride frequency, waddling and leaning amplitude, as well as the leaning angle to be defined. The magnitude of the measured variables showed a significant increase with increasing speed, apart from the backwards angle of lean, which decreased with increasing speed. The variability of the measured variables also showed a significant increase with speed apart from the DBAz amplitude, the waddling amplitude, and the leaning angle, where no significant effect of the walking speed was found. This paper is the first approach to describe 3D biomechanics with an accelerometer on wild animals, demonstrating the potential of this technique.

Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning

BACKGROUND

Research on wild animal ecology is increasingly employing GPS telemetry in order to determine animal movement. However, GPS systems record position intermittently, providing no information on latent position or track tortuosity. High frequency GPS have high power requirements, which necessitates large batteries (often effectively precluding their use on small animals) or reduced deployment duration. Dead-reckoning is an alternative approach which has the potential to 'fill in the gaps' between less resolute forms of telemetry without incurring the power costs. However, although this method has been used in aquatic environments, no explicit demonstration of terrestrial dead-reckoning has been presented.

RESULTS

We perform a simple validation experiment to assess the rate of error accumulation in terrestrial dead-reckoning. In addition, examples of successful implementation of dead-reckoning are given using data from the domestic dog Canus lupus, horse Equus ferus, cow Bos taurus and wild badger Meles meles.

CONCLUSIONS

This study documents how terrestrial dead-reckoning can be undertaken, describing derivation of heading from tri-axial accelerometer and tri-axial magnetometer data, correction for hard and soft iron distortions on the magnetometer output, and presenting a novel correction procedure to marry dead-reckoned paths to ground-truthed positions. This study is the first explicit demonstration of terrestrial dead-reckoning, which provides a workable method of deriving the paths of animals on a step-by-step scale. The wider implications of this method for the understanding of animal movement ecology are discussed.

The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber

Recent technological innovations have led to the development of miniature, accelerometer-containing electronic loggers which can be attached to free-living animals. Accelerometers provide information on both body posture and dynamism which can be used as descriptors to define behaviour. We deployed tri-axial accelerometer loggers on 12 free-ranging Eurasian beavers Castor fiber in the county of Telemark, Norway, and on four captive beavers (two Eurasian beavers and two North American beavers C. canadensis) to corroborate acceleration signals with observed behaviours. By using random forests for classifying behavioural patterns of beavers from accelerometry data, we were able to distinguish seven behaviours; standing, walking, swimming, feeding, grooming, diving and sleeping. We show how to apply the use of acceleration to determine behaviour, and emphasise the ease with which this non-invasive method can be implemented. Furthermore, we discuss the strengths and weaknesses of this, and the implementation of accelerometry on animals, illustrating limitations, suggestions and solutions. Ultimately, this approach may also serve as a template facilitating studies on other animals with similar locomotor modes and deliver new insights into hitherto unknown aspects of behavioural ecology.

Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm

Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

From physiology to physics: are we recognizing the flexibility of biologging tools?

The remote measurement of data from free-ranging animals has been termed ‘biologging’ and in recent years this relatively small set of tools has been instrumental in addressing remarkably diverse questions – from ‘how will tuna respond to climate change?’ to ‘why are whales big?’. While a single biologging dataset can have the potential to test hypotheses spanning physiology, ecology, evolution and theoretical physics, explicit illustrations of this flexibility are scarce and this has arguably hindered the full realization of the power of biologging tools. Here we present a small set of examples from studies that have collected data on two parameters widespread in biologging research (depth and acceleration), but that have interpreted their data in the context of extremely diverse phenomena: from tests of biomechanical and diving-optimality models to identifications of feeding events, Lévy flight foraging strategies and expanding oxygen minimum zones. We use these examples to highlight the remarkable flexibility of biologging tools, and identify several mechanisms that may enhance the scope and dissemination of future biologging research programs.

Climate and the individual: inter-annual variation in the autumnal activity of the European badger (Meles meles)

We establish intra-individual and inter-annual variability in European badger (Meles meles) autumnal nightly activity in relation to fine-scale climatic variables, using tri-axial accelerometry. This contributes further to understanding of causality in the established interaction between weather conditions and population dynamics in this species. Modelling found that measures of daylight, rain/humidity, and soil temperature were the most supported predictors of ACTIVITY, in both years studied. In 2010, the drier year, the most supported model included the SOLAR*RH interaction, RAIN, and30cmTEMP (w = 0.557), while in 2012, a wetter year, the most supported model included the SOLAR*RH interaction, and the RAIN*10cmTEMP (w = 0.999). ACTIVITY also differed significantly between individuals. In the 2012 autumn study period, badgers with the longest per noctem activity subsequently exhibited higher Body Condition Indices (BCI) when recaptured. In contrast, under drier 2010 conditions, badgers in good BCI engaged in less per noctem activity, while badgers with poor BCI were the most active. When compared on the same calendar dates, to control for night length, duration of mean badger nightly activity was longer (9.5 hrs ±3.3 SE) in 2010 than in 2012 (8.3 hrs ±1.9 SE). In the wetter year, increasing nightly activity was associated with net-positive energetic gains (from BCI), likely due to better foraging conditions. In a drier year, with greater potential for net-negative energy returns, individual nutritional state proved crucial in modifying activity regimes; thus we emphasise how a ‘one size fits all’ approach should not be applied to ecological responses.

AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements

BACKGROUND

The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data.

DESCRIPTION

Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models.

CONCLUSIONS

AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

On higher ground: how well can dynamic body acceleration determine speed in variable terrain?

Introduction

Animal travel speed is an ecologically significant parameter, with implications for the study of energetics and animal behaviour. It is also necessary for the calculation of animal paths by dead-reckoning. Dead-reckoning uses heading and speed to calculate an animal’s path through its environment on a fine scale. It is often used in aquatic environments, where transmission telemetry is difficult. However, its adoption for tracking terrestrial animals is limited by our ability to measure speed accurately on a fine scale. Recently, tri-axial accelerometers have shown promise for estimating speed, but their accuracy appears affected by changes in substrate and surface gradients. The purpose of the present study was to evaluate four metrics of acceleration; Overall dynamic body acceleration (ODBA), vectorial dynamic body acceleration (VDBA), acceleration peak frequency and acceleration peak amplitude, as proxies for speed over hard, soft and inclined surfaces, using humans as a model species.

Results

A general linear model (GLM) showed a significant difference in the relationships between the metrics and speed depending on substrate or surface gradient. When the data from all surface types were considered together, VeDBA had the highest coefficient of determination.

Conclusions

All of the metrics showed some variation in their relationship with speed according to the surface type. This indicates that changes in the substrate or surface gradient during locomotion by animals would produce errors in speed estimates, and also in dead-reckoned tracks if they were calculated from speeds based entirely on a priori calibrations. However, we describe a method by which the relationship between acceleration metrics and speed can be corrected ad hoc, until tracks accord with periodic ground truthed positions, obtained via a secondary means (e.g. VHF or GPS telemetry). In this way, dead-reckoning provides a means to obtain fine scale movement data for terrestrial animals, without the need for additional data on substrate or gradient.

Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus)

We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be 83%-94%, but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.

Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures

Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80-90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.

The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems

Numerous methods are currently available to track animal movements. However, only one of these, dead-reckoning, has the capacity to provide continuous data for animal movements over fine scales. Dead-reckoning has been applied almost exclusively in the study of marine species, in part due to the difficulty of accurately measuring the speed of terrestrial species. In the present study we evaluate the use of accelerometers and a metric known as overall dynamic body acceleration (ODBA) as a proxy for the measurement of speed for use in dead-reckoning. Data were collated from previous studies, for 10 species locomoting on a treadmill and their ODBA measured by an attached data logger. All species except one showed a highly significant linear relationship between speed and ODBA; however, there was appreciable inter- and intra-specific variance in this relationship. ODBA was then used to estimate speed in a simple trial run of a dead-reckoning track. Estimating distance travelled using speed derived from prior calibration for ODBA resulted in appreciable errors. We describe a method by which these errors can be minimised, by periodic ground-truthing (e.g., by GPS or VHF telemetry) of the dead-reckoned track and adjusting the relationship between speed and ODBA until actual known positions and dead-reckoned positions accord.

Assessing the validity of the accelerometry technique for estimating the energy expenditure of diving double-crested cormorants Phalacrocorax auritus

Over the past few years, acceleration-data loggers have been used to provide calibrated proxies of energy expenditure: the accelerometry technique. Relationships between rate of oxygen consumption and a derivation of acceleration data termed "overall dynamic body acceleration" (ODBA) have now been generated for a range of species, including birds, mammals, and amphibians. In this study, we examine the utility of the accelerometry technique for estimating the energy expended by double-crested cormorants Phalacrocorax auritus to undertake a dive cycle (i.e., a dive and the subsequent pause at the surface before another dive). The results show that ODBA does not calibrate with energy expenditure in diving cormorants, where energy expenditure is calculated from measures of oxygen uptake during surface periods between dives. The possible explanations include reasons why energy expenditure may not relate to ODBA but also reasons why oxygen uptake between dives may not accurately represent energy expenditure during a dive cycle.

Assessing the development and application of the accelerometry technique for estimating energy expenditure

A theoretically valid proxy of energy expenditure is the acceleration of an animal's mass due to the movement of its body parts. Acceleration can be measured by an accelerometer and recorded onto a data logging device. Relevant studies have usually derived a measure of acceleration from the raw data that represents acceleration purely due to movement of the animal. This is termed 'overall dynamic body acceleration' (ODBA) and to date has proved a robust derivation of acceleration for use as an energy expenditure proxy. Acceleration data loggers are generally easy to deploy and the measures recorded appear robust to slight variation in location and orientation. This review discusses important issues concerning the accelerometry technique for estimating energy expenditure and ODBA; deriving ODBA, calibrating ODBA, acceleration logger recording frequencies, scenarios where ODBA is less likely to be valid, and the power in recording acceleration and heart rate together. While present evidence suggests that ODBA may not quantify energy expenditure during diving by birds and mammals, several recent studies have assessed changes in mechanical work in such species qualitatively through variation in ODBA during periods of submergence. The use of ODBA in field metabolic studies is likely to continue growing, supported by its relative ease of use and range of applications.

A versatile telemetry system for continuous measurement of heart rate, body temperature and locomotor activity in free-ranging ruminants

1. Measuring physiological and behavioural parameters in free-ranging animals - and therefore under fully natural conditions - is of general biological concern but difficult to perform.2. We have developed a minimally invasive telemetry system for ruminants that is capable of measuring heart rate (HR), body temperature (T(b)) and locomotor activity (LA). A ruminal transmitter unit was per os placed into the reticulum and therefore located in close proximity to the heart. The unit detected HR by the use of an acceleration sensor and also measured T(b). HR and T(b) signals were transmitted via short-distance UHF link to a repeater system located in a collar unit. The collar unit decoded and processed signals received from the ruminal unit, measured LA with two different activity sensors and transmitted pulse interval-modulated VHF signals over distances of up to 10 km.3. HR data measured with the new device contained noise caused by reticulum contractions and animal movements that triggered the acceleration sensor in the ruminal unit. We have developed a software filter to remove this noise. Hence, the system was only capable of measuring HR in animals that showed little or no activity and in the absence of rumen contractions. Reliability of this 'stationary HR' measurement was confirmed with a second independent measurement of HR detected by an electrocardiogram in a domestic sheep (Ovis aries).4. In addition, we developed an algorithm to correctly classify an animal as 'active' or 'at rest' during each 3-min interval from the output of the activity sensors. Comparison with direct behavioural observations on free-ranging Alpine ibex (Capra ibex) showed that 87% of intervals were classified correctly.5. First results from applications of this new technique in free-ranging Alpine ibex underlined its suitability for reliable and long-term monitoring of physiological and behavioural parameters in ruminants under harsh field conditions. With the battery settings and measurement cycles used in this study, we achieved a system lifetime of approximately 2 years.