Open Access

Observing the unwatchable through acceleration logging of animal behavior

  • Danielle D Brown1Email author,
  • Roland Kays2, 3, 4,
  • Martin Wikelski4, 5, 6,
  • Rory Wilson7 and
  • A Peter Klimley8
Animal Biotelemetry20131:20

DOI: 10.1186/2050-3385-1-20

Received: 23 July 2013

Accepted: 7 October 2013

Published: 10 December 2013

Abstract

Behavior is an important mechanism of evolution and it is paid for through energy expenditure. Nevertheless, field biologists can rarely observe animals for more than a fraction of their daily activities and attempts to quantify behavior for modeling ecological processes often exclude cryptic yet important behavioral events. Over the past few years, an explosion of research on remote monitoring of animal behavior using acceleration sensors has smashed the decades-old limits of observational studies. Animal-attached accelerometers measure the change in velocity of the body over time and can quantify fine-scale movements and body postures unlimited by visibility, observer bias, or the scale of space use. Pioneered more than a decade ago, application of accelerometers as a remote monitoring tool has recently surged thanks to the development of more accessible hardware and software. It has been applied to more than 120 species of animals to date. Accelerometer measurements are typically collected in three dimensions of movement at very high resolution (>10 Hz), and have so far been applied towards two main objectives. First, the patterns of accelerometer waveforms can be used to deduce specific behaviors through animal movement and body posture. Second, the variation in accelerometer waveform measurements has been shown to correlate with energy expenditure, opening up a suite of scientific questions in species notoriously difficult to observe in the wild. To date, studies of wild aquatic species outnumber wild terrestrial species and analyses of social behaviors are particularly few in number. Researchers of domestic and captive species also tend to report methodology more thoroughly than those studying species in the wild. There are substantial challenges to getting the most out of accelerometers, including validation, calibration, and the management and analysis of large quantities of data. In this review, we illustrate how accelerometers work, provide an overview of the ecological questions that have employed accelerometry, and highlight the emerging best practices for data acquisition and analysis. This tool offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and, across scientific disciplines, it improves understanding of the role of behavioral mechanisms in ecological and evolutionary processes.

Keywords

Accelerometer Activity Animal behavior Bio-logging Dead reckoning Energy expenditure Ethogram Remote observation Telemetry

Abstract

Resumen

El comportamiento es un mecanismo importante de la evolución y que se paga a través del gasto de energía. Sin embargo, los biólogos de campo raramente observan los animales durante más de una fracción de sus actividades y los intentos de cuantificar el comportamiento para el modelado de los procesos ecológicos a menudo excluyen eventos crípticos pero importantes. En los últimos años se produjeron avances importantes en el monitoreo remoto del comportamiento de los animales, utilizando sensores de telemétro de aceleración (acelerómetros) que empujan los límites tradicionales de los estudios observacionales. Acelerómetros unidos a los animales miden el cambio de la velocidad del cuerpo en el tiempo y pueden cuantificar los movimientos a escala fina y posturas corporales ilimitadas por la visibilidad, el sesgo del observador, o la escala de la utilización del espacio. Como pionero hace más de una década, la aplicación de los acelerómetros como una herramienta de monitoreo remoto ha aumentado recientemente debido al desarrollo de hardware y software más accesibles. Se ha aplicado a más de 120 especies de animales hasta hoy. Medidas de los acelerómetros se recogen típicamente en tres dimensiones de movimiento a muy alta resolución (>10 Hz), y hasta ahora se han aplicado hacia dos objetivos principales. Primero, los patrones de las formas de los acelerómetros de onda se pueden utilizar para deducir comportamientos específicos a través de movimiento de los animales y la postura corporal. Segundo, se ha demonstrado que la variación en las medidas de forma de los acelerómetros de onda se ha demostrado que se correlaciona con el gasto de energía, abriendo una serie de preguntas de carácter científico sobre especies muy difíciles de observar en la naturaleza. Hasta la fecha, los estudios de las especies acuáticas silvestres superan a las especies terrestres silvestres, y los análisis de los comportamientos sociales son muy pocos en número. Los investigadores de las especies domésticas y en cautiverio tienden a reportar metodología más completa que los que estudian las especies silvestres. Hay retos importantes para conseguir el máximo rendimiento de los acelerómetros, incluyendo la validación, calibración y gestión y análisis de grandes cantidades de datos. En esta revisión se ilustra cómo funciona el acelerómetro, se proporciona una visión general de las investigaciones ecológicas que han empleado los acelerómetros y se destacan las mejores prácticas emergentes para la adquisición y análisis de datos. Esta herramienta ofrece un nivel de detalle en los estudios de comportamiento de los animales salvajes que han sido hasta ahora imposibles de alcanzar y, en todas las disciplinas científicas, que mejora la comprensión del papel de los mecanismos de comportamiento de los procesos ecológicos y evolutivos.

Palabras claves

Acelerómetro, actividad, bio-registro, comportamiento animal, gasto energético, etograma, navegación a estima, observación a distancia, telemetría.

Keywords

Accelerometer Activity Animal behavior Bio-logging Dead reckoning Energy expenditure Ethogram Remote observation Telemetry

Review

Man goes to nature to learn what nature is, but, in so doing, he introduces possibilities of distortion through his own presence.” – T.C. Schneirla (p. 1022, [1]).

Naturalists have long been aware that their presence can affect animal behavior [1, 2]. Direct observation presents obvious difficulties when animals perceive humans as predators [3] or when they are naturally secretive and elusive [4, 5]. Habituating individuals to an observer is sometimes possible but it is labor-intensive, and can require long-term study [6, 7]. Furthermore, though the subjects under study may be habituated, human presence can still affect their behavioral interactions with other non-habituated predator, prey or competitor species [8]. The observer is rarely undetectable and even animals that do not appear to react to human presence may still change their behavior in subtle ways [9, 10]. Direct observations are also biased by our own physical limitations [1113] and tendencies to attend to some events and subjects more than others [14].

The field of biotelemetry grew out of the need to locate animals at will and observe and record their habits despite their abilities to travel rapidly and widely in inclement weather, underwater, or at night [12, 13, 15]. Locating animals in space has progressed from manual tracking of animal-borne radio- or acoustic signals to automated depth and geomagnetic loggers and satellite-based positioning systems that practically eliminate the observer effect and can now provide precise worldwide locations with few temporal or spatial constraints [11, 16]. Nevertheless, a record of animal locations or a depth profile tells where the animal was and how long it stayed there, but the behavioral context is absent and must either be inferred or demands a return to direct observation methods [17]. These issues underscore the need for remote measurement of animal behavior to reduce or eliminate the potential effects of observer presence while maintaining a high level of detail in data recording that is comparable to direct observation [18]. Over the past few years, there has been an explosion of research on remote monitoring of animal behavior using measurements of acceleration (Figure 1) [19, 20]. This tool, the accelerometer, has repeatedly circumvented many of the age-old limits of direct observation of animals in the field.
Figure 1

Primary papers using accelerometers in animal behavior research 1998–2012.

Figure 2 provides a basic explanation of how an accelerometer works [21]. An accelerometer is a spring-like piezoelectric sensor. When deformed, the sensor generates a wave-like voltage signal that is proportional to the acceleration (change in velocity) it experiences [22]. The sensor is deformed both by gravitational acceleration as well as inertial acceleration due to movement. From one to three of these sensors are aligned orthogonally to one another and affixed to an animal so that each sensor measures acceleration in a single plane, or dimension, of movement (surge, heave, and sway (Figure 2)). All three sensors collecting simultaneous measurements can represent three-dimensional movement realistically [20, 23]. The sensors can be user-programmed to sample acceleration at frequencies ranging from 0.5 to 10,000 Hz, and can be set to record continuously or in repeated bursts (e.g., every 2 min). The voltage signals, also known as raw accelerometer output, may be used in their raw state, or converted to actual acceleration if the unit is carefully calibrated (measured in g; 1 g = 9.8 m/s2). Under static circumstances, such as during rest or after death, the accelerometer signal only represents the gravitational force acting on the sensors. When an animal is moving, sensor output represents acceleration due to gravity combined with the inertial acceleration generated by movement [23]. Accelerometers typically incorporate a microprocessor and digital memory to store logged measurements until the instrument is retrieved [24].
Figure 2

How an accelerometer works and typical orientation of instrument axes on a terrestrial mammal ( Tamandua mexicana ) [ [21] ].

Measurement of acceleration is a well-established research tool in biomechanics [25, 26] and exercise science [27, 28]. The first (wired) accelerometers were used to examine the biomechanics of movement in humans [29] and fish [30] and then to ascertain the correlation between bodily acceleration and oxygen consumption in human subjects [28]. The introduction of air-bag technology in passenger vehicles lead to the development of relatively inexpensive accelerometers that use very little power [31]. These were quickly adopted for studies outside the laboratory environment, because they are “small, low-cost instruments that provide quantitative [and objective] measurements [of activity]” p. 679, [32]. Animal studies using these modern, truly portable acceleration sensors did not appear in the literature until the late 1990s [33, 34]. Initially, animal studies were confined to captive and domesticated species, as well as aquatic taxa, for whom few other behavioral observation methods were possible [12, 35, 36]. Since then, the ongoing reduction in the size of computer microprocessors and improvements in battery size, weight, and longevity combined with these small solid-state acceleration sensors have resulted in a modern accelerometer that can weigh 0.7 g (without a battery) and measure 9.5 × 15 × 4 mm (available from: http://www.technosmart.eu/axy.php). Modern accelerometers also consume very little digital memory with each measurement [37], so data collection and data storage on-board the instrument itself is possible for up to several months or years, depending on the sampling schedule. Accelerometers that simply log their data must be retrieved after the sampling period, as with other types of telemetric data loggers. However, some accelerometers incorporate ultra-high frequency data download technology in similar frequency bandwidths as those used in cellular phones [38]. This feature makes it possible to download the data from the accelerometer from a reasonable distance (up to 500 m, personal observation) even if the device and its bearer are not visible or the instrument cannot be retrieved because it has been discarded in a tree cavity, for example [21]. Radio or acoustic beacons are commonly used on loggers that must be retrieved [21, 24].

Movement is the fundamental behavioral response to both internal motivations and the external environment [13, 17]. Using accelerometers, biologists can measure the movement behavior of wild animals over biologically and ecologically significant events and periods, practically unlimited by visibility, observer bias, or geographic scale. Accelerometers can be deployed with other sensors, such as those recording location (GPS, acoustic telemetry, water depth), physiological measurements (heart rate, body temperature), and environmental variables like air temperature, light levels and magnetic heading [24, 39, 40]. Particularly when combined with other instruments, measurements of acceleration can provide a wide range of detailed information on the environmental context of animal behavior and physiology that can exceed the descriptive abilities of the human observer and deepen our knowledge even for well-known species such as domestic animals. Here, we review how accelerometers have been used to date in the study of animal behavior, including the taxonomic and research trends in the literature and we illustrate the type of data produced by this technology from instruments deployed on a variety of species. Further, we provide a summary of the currently available techniques for data calibration, management and analysis, and suggest key directions for future research.

Methods

We accessed BIOSIS® Previews and ISI Web of Knowledge® online and ran searches for any publication containing references to accelerometry in the title, abstract or keywords. We limited our analysis to primary research published in peer-reviewed journals and book chapters through December 2012. From those, we selected studies utilizing animal-borne sensors applied to non-human species. We assessed the resulting works for the following: i) study purpose; ii) species and whether subjects were captive/domestic or free-ranging, and aquatic or terrestrial; iii) number of acceleration axes; iv) sampling frequency utilized; v) the behavioral resolution of the resulting measurements; vi) the parameters of the accelerometer data used for analysis; vii) whether or not behavioral classification accuracy was reported (if pertinent); and viii) whether accelerometry was combined with other telemetry sensors. Results are presented as percentages; not all percentages will sum to 100 because not all categories were mutually exclusive.

Results

We discovered 176 accelerometry studies and counted 125 animal species that have borne accelerometers (Additional file 1). Studies were relatively evenly split between aquatic (48.3%) and terrestrial (52.8%) habitats and between free-ranging wild animals (50%) and domesticated/captive wild animals (33/27.3%), but there were biases among taxa for these categories (Figures 3 and 4). Mammals represented 45.6% of all study species with domestic cattle and Pinnipeds being the most-studied among the mammals (14% of studies and 18% of species, respectively). Birds comprised 33.6% of all study species and 38% of avian species were either Sphenisciformes or Suliformes. Fishes included 11.2% of species and half of all fish species were Elasmobranch sharks. Eight reptile species, five of them Chelonians, comprised 6.4% of study subjects. Giant cuttlefish (Sepia apama), Humboldt squid (Dosidicus gigas), King scallop (Pecten maximus) and Cane toad (Bufo marinus) were the four study species remaining outside of these four taxon categories.
Figure 3

Accelerometry studies performed on wild free-ranging animals compared to domestic/captive animals by taxon.

Figure 4

Accelerometry studies performed on aquatic animals compared to terrestrial animals by taxon.

More than half of all studies (62.3%) utilized 3-axis accelerometers; 90.3% of studies utilized either 2- or 3-axis accelerometers. Sampling frequencies ranged from 0.5 Hz to 10,000 Hz, with 60% of studies using one of the following most common sampling frequencies of 8, 10, 16, 32, 64 or 100 Hz. Forty-eight percent of studies collected acceleration data continuously and 13.3% collected data in discrete bursts or intervals; 38.7% of studies did not clearly report their collection method. Sixty-three percent of studies combined an accelerometer with other telemetric instruments; however, free-ranging wild species were 3 times more likely than captive wild species and 6 times more likely than domesticated species to be outfitted with telemetry devices that contained multiple sensors. The most common remote sensors used in tandem with accelerometers measured depth (35.6% of studies), travel speed (16% of studies) and temperature (14.7% of studies).

Survey of questions currently served by accelerometry: body posture and body movement

The acceleration waveforms over short (millisecond) to long (minutes) periods can be used to deduce behavior-specific body postures and body movements (Figures 5 and 6) [41, 42]. Across taxa, 36.4% of studies reported acceleration ethograms or acceleration-based descriptions of behavior. Just under half (46.6%) of all studies utilized the accelerometer waveforms to determine activity budgets. As shown graphically in Figure 5, accelerometer voltage output of inactive or rest behavior is more or less constant, while whole-body movement of any kind produces fluctuating acceleration waveforms with high levels of variance among measurements. Of the studies examining activity budgets, 35% of authors used this variance characteristic of accelerometer waveforms to simply identify the timing of activity vs. rest [4346]. Sixty-five percent of authors identified distinct waveforms for specific behaviors and then estimated the amount of time animals spent engaged in these behaviors such as chasing prey or feeding, flight, swimming, walking, running, climbing, standing, lying down, thermoregulation and sleeping. Quantifying foraging effort is an application of accelerometry that few other telemetry technologies can accomplish and is particularly useful for animals that forage or hunt out of sight. Researchers have documented foraging strategies that differ by species, age or sex [4752]. Other studies placed accelerometer sensors on the head/mandible to directly measure attempts at food capture [53], although foraging effort did not necessarily correlate with foraging success [54]. There are several methods for identifying and categorizing waveforms that represent specific behaviors (see ‘Best Practices’, below). On average, these studies were able to identify four distinct acceleration waveform profiles (range 2 to 7), typically falling under the broad behavioral categories of locomotion, resting, and feeding/foraging [5557]. In general, while the accelerometer patterns of active locomotory behaviors (walking, running, climbing, swimming and flying) are clearly distinguishable from inactive behaviors such as sleep, thermoregulation and digestion, the waveforms of these types of relatively immobile behaviors are not particularly distinct from one another [19].
Figure 5

Accelerometer-based determination of body posture and the timing of rest vs. activity. Data are from a study of the northern tamandua anteater Tamandua mexicana[21]. Acceleration was sampled at 19 Hz for ~3 seconds every 2 min. For simplicity, the y-axis is not shown and waveforms represent the average voltage measured every 2 min for each axis.

Figure 6

Heave-axis acceleration waveforms of three behaviors of the Swallow-tailed gull (Creagrus furcatus) . The y-axis shows the unit-free voltage output of the accelerometer sensor. Photograph and data prepared by Sebastián Cruz (unpublished).

When an accelerometer is combined with other sensors on a tagged animal, researchers can describe the broader ecological context of accelerometer-determined behaviors. Light level and ambient temperature sensors in tandem with accelerometry permit examination of activity timing in relation to environmental conditions [5861]. Accelerometers and remotely-sensed location via GPS, compass, depth or acoustic sensors provide the spatial distributions of accelerometer-determined behaviors [6266] and can lead to novel insights about species’ behavioral ecology. For example, traditional observation-only research of the little-known oilbird (Steatornis caripensis) led to the hypothesis that these nocturnal frugivorous birds were not seed dispersers because the seeds from their diet were regurgitated in the dark caves where the birds roost. Holland et al. [38] determined that oilbirds outfitted with GPS/acceleration loggers spent only every third day in caves, otherwise remaining in the rainforest where they regurgitated seeds onto the forest floor at considerable distances from both feeding sites and cave roosts. The authors maintained that oilbirds should be reconsidered as an important long-distance seed disperser in Neotropical forests, a novel hypothesis for the ecology of that ecosystem.

Behavioral analysis applied to monitor animal welfare was a significant component of accelerometry research on terrestrial animals; 80% of the terrestrial studies (and 25% of all studies) examined the welfare of domesticated species. Typically, authors used accelerometry to monitor behavioral changes associated with reproduction [67, 68] or behavioral responses to veterinary or husbandry practices [6974]. In studying welfare of free-ranging wild species, mortality sensors are a common feature of telemeters and typically provide a special signal to alert researchers to the animal’s demise [75]. The advantage of using accelerometers to detect mortality is that it includes a record of behavior leading up to the time of death, providing a richer context that a simple location and time of death often do not. For example, Krone et al. [76] were able to identify a change in activity and, ultimately, the moment of death, due to toxin exposure in a white-tailed sea eagle (Haliaeetus albicilla).

What is largely absent from this body of 82 articles about activity budgets is the measurement of social behaviors. While numerous studies compared behavior budgets during particular reproductive states and reported ethograms for brooding or nest preparation [52, 57, 66, 7781], only two studies examined whether mating behavior had a characteristic acceleration profile [82, 83]. The scarcity of published accelerometry ethograms for aggressive interactions, territorial or courtship displays, and play and parent-offspring behavior [84, 85] could be because these social behaviors were generally rare in the majority of the species that have been studied, the acceleration waveforms of social behaviors were indistinguishable from those of non-social behaviors, or because it was not feasible to tag multiple animals in the same group. Inter-individual telemetry, with animals bearing tags that are able to record the date and time of proximity to other tagged animals has recently been reported for acoustic transmitters [86]. The application of accelerometers to studies of social behavior would benefit mightily from accelerometer tags that have the ability to record proximity, identity or even behavior of tagged individuals in contact with the animal that bears the primary tag.

Survey of questions currently served by accelerometry: biomechanics and the energetics of movement

Energetics have long been of interest to behavioral ecologists [87, 88] because all movements require energy, and prudent allocation of energy to specific activities such as foraging has direct consequences for fitness and natural selection [77, 89, 90]. Prior to the recent developments in accelerometry, measuring energy expenditure in wild animals in the field involved doubly-labeled water or heart rate telemetry, both of which have logistical limitations that have restricted their use [91, 92]. Accelerometer technology has dramatically advanced our understanding of the role of energy in behavioral strategies by making it possible to study fine-scale, behavior-specific energy expenditure outside the laboratory in diverse taxa [93]. Wilson and Halsey et al. have tested for correlations between bodily acceleration and oxygen consumed (assuming at least predominantly aerobic metabolic pathways) across a wide range of species from aquatic mammals [94, 95], birds [64, 77, 9699], fishes [100, 101], reptiles [52, 102104] and a bivalve [105], to terrestrial mammals, birds [56, 106108] and one amphibian [109]. Although the strength of the relationship between bodily acceleration and oxygen consumption (as a proxy for metabolic rate) varies and depends on a number of factors, the relationship is valid across all species examined to date [110]. Wilson’s metric ‘Overall Dynamic Body Acceleration’ (ODBA) [77] has become the most commonly used acceleration-based proxy of metabolic rate (energy expenditure) and several articles have been devoted to standardizing this proxy or variants of it [19, 106, 110112]. The current available research indicates that bodily acceleration can qualitatively assess how the amount of mechanical work performed by the body differs among active locomotive behaviors, a distinct improvement on older techniques that were not behavior-specific (Figure 7) [110].
Figure 7

Overall dynamic body acceleration shown for a hopping and non-hopping cane toad Bufo marinus . This study was the first to use accelerometry to establish a behavioral time budget and assign energy costs to those behaviors for a non-volant terrestrial animal. Graphic reprinted with permission from [109].

Seventy-three articles applied accelerometry to biomechanical research (42.7% of all articles examined). A small minority of these studies (7), eschewed applications to metabolism and instead remained within the traditional realm of evaluating performance: running in racehorses [113117], swimming in sea snakes [118], and flight in Procellariform sea birds [119]. The remaining 90% of articles focused on energy efficiency during locomotion for travel or foraging [31, 35, 94, 120124]. In order to better understand the selection pressures on current patterns of locomotor behavior, researchers compared movement energetics across species, movement strategies, demographic classes and behaviors [42, 48, 102, 107, 122127].

There was a strong habitat bias in existing accelerometry-based research on biomechanical energetics, with a heavy emphasis on marine diving animals such as Pinnipeds and Cetaceans [36, 95, 128, 129], penguins [97, 130, 131], Pelicaniform birds [77, 99, 126, 131133], and marine turtles [79, 134]. Terrestrial taxa, mainly birds, were represented in only five (of 66) studies of movement energetics [56, 106108]. Of the terrestrial species, we were able to identify only a single published field study of energetics for non-volant terrestrial animals: cane toads (Bufo marinus) [109]. Battery size and weight still mostly preclude accelerometry energetics studies of the smallest wild mammals (particularly bats) and birds. A further limitation is that accelerometers do not appear to be a particularly good proxy of energy expended during immobile but still energetically costly behaviors such as thermoregulation or gestation [107, 135].

Potential application of accelerometry: position and location

Acceleration measurements can be used to derive animal speed, which, together with compass and depth/altitudinal information, could be used to ‘dead-reckon’ an animal’s position. There are several existing methods for locating animals in space and time including radio telemetry [136], satellite or geographic positioning systems [11, 137, 138] and acoustic arrays [139]. None of these methods works for all species and habitats and, consequently, travel paths are frequently reconstructed by bridging sporadic points and have low spatio-temporal resolution [140, 141].

Dead-reckoning (also known as path integration) uses vector calculations from velocity and the change in height or depth together with a known start position (usually the animal release point) to derive new positions with respect to those previously known [24, 142]. Locations obtained by dead-reckoning, therefore, are not subject to the same constraints of receiver location or satellite access and represent an alternative method for studying movement paths when radio- or satellite-based telemetry methods are unsuitable. Dead-reckoning uses sensors on-board the telemetry tag that record heading/direction (usually measured with magnetometers), altitude or depth (usually measured with pressure sensors), and speed. In theory, speed can be calculated by taking the derivative of acceleration over a known time interval [130] or by using a known stride length and the accelerometer-measured stride frequency [142]. However, speed determined in this way can be subject to large errors due to variation in slope and substrate during travel [67, 139]. These errors are particularly unpredictable in aquatic or volant species, due to drift caused by water and wind currents rather than animal locomotion [142]. In terrestrial systems, terrain incline and substrate impact stride length, affect speed calculations and consequently the determination of distance moved. Furthermore, these errors accumulate over time, making location estimates increasingly worse further from the last known location. Because of these problems, dead-reckoning from accelerometry data has been used infrequently and most researchers interested in movement speed have added separate speed sensors (small external propellers) to the telemetry tags [24, 137, 141144]. As GPS technology becomes more widely integrated into accelerometer tags, the greatest potential for dead-reckoned animal location comes in recreating the exact travel path between subsequent GPS locations collected at short intervals e.g., <15 min [24, 139].

Best practices in data acquisition and data analysis: tag attachment and tandem sensors

Attaching telemetry tags to animals is a complicated and delicate process that requires care to reduce the influence of the equipment to the animal. Consultation with experienced field biologists and tag companies, not to mention proper literature review, is critical during the planning stage of any tagging study. In addition to the standard concerns of attachment longevity, device retrieval and whether tag attachment affects animal behavior [138], tag attachment for accelerometer sensors is especially sensitive because shifts of the tag relative to the position of the animal could impact the interpretation of the three-axis data. Extensive preliminary research on readily observable animals is often needed to fine-tune a new attachment method for a given species [145]. Common methods of accelerometer tag attachment include neck collars [20, 55], leg bracelets [146], harnesses [109, 145], and tape- [118], clamp- [147] or glue-on tags (Figure 2) [21, 102]. A rigid attachment ensures that once the tag is deployed, the axes, or dimensions, of movement being measured do not change over the deployment period and that acceleration of the tag independent of the animal (by a collar bouncing up and down on the neck, for example) is kept to a minimum [111]. For species that must wear collars or bracelets, a completely rigid attachment is not possible unless the collar can be prevented from turning around the neck/leg [55], which may present a welfare concern for free-ranging animals. For some questions, for example the timing of activity/rest, the requirement of rigid attachment may be relaxed. Finally, accelerometer tags can also be deployed inside the body cavity of some species [61, 148], which may reduce concerns about tag movements that are irrelevant to the research question. Internal deployments may provide the advantage of recording accelerations due to physiological processes such as heartbeat and movements of smooth muscle during digestion [148], but can also have the disadvantage of necessitating surgical procedures for tag deployment and retrieval/removal, which can affect animal behavior and well-being.

The orientation of axes is typically placed so that the surge axis is aligned with the longitudinal body axis and sway with the horizontal body axis (Figure 2) [20]. Ensuring that tag position is as similar as possible between individuals, especially those of very different body sizes, improves the signal-to-noise ratio of the accelerometer output and minimizes errors in interpretation [20, 63, 101, 131]. Beyond its orientation on the body, the specific anatomical location of the attached accelerometer tag largely determines, what behaviors can be distinguished by their accelerometry patterns. Both species morphology and tag placement will determine the number and type of behaviors with distinct acceleration profiles [44]. For example, tags attached to an animal’s back, as in Figure 2 [38, 109], will not provide acceleration patterns of fine-scale feeding behaviors that only involve movement of the mouth. On the other hand, accelerations of chewing movements may be detectable with neck collars [33, 55]. In humans, it has been well established that precise accelerometer-based descriptions of full-body movement require at least five acceleration sensors, one mounted on the trunk of the body and one on each extremity [26]. Studies of free-ranging wild animal are typically limited to one telemetry tag per individual; however, multiple accelerometer instruments have been used on domesticated animals [149152] and in a handful of wild marine species [53, 54, 153157], improving the precision of behavior measurements.

Even when contained in a single tag, most modern accelerometers are combined with other types of sensors to enhance the amount of information collected simultaneously from the environment, such as light, air/water pressure, external air/water temperature, relative humidity and magnetic field [24], as well as from the animal, such as body temperature, heart rate and mouth/jaw movements [79, 99, 101, 133, 134, 157160]. In modern telemetry tags, each of these data sensors, including each axis of the accelerometer, have their own separate channels for data recording, so that accelerometer data are collected independently of other information like GPS [24, 160]. As a result, even if one sensor malfunctions or cannot acquire information momentarily (for example, the GPS unit spends several minutes attempting to access satellites and obtain a location), the other sensors continue to record data on schedule. In some tags, the activity levels of the animal as determined by the accelerometer can be used to set the recording schedules of other sensors dynamically. For example, the GPS schedule is set to acquire locations more frequently during active behaviors such as foraging and travel and less frequently during rest, improving the overall performance and battery longevity of the telemeter [21].

Best practices in data acquisition and data analysis: sampling axes, sampling interval and sampling frequency

Sampling of all three axes of acceleration (tri-axial) is the most accurate and precise way of measuring behavior that occurs in three dimensions as well as estimating energy expenditure [20, 161]. For some research questions or for relatively immobile species, one or two axes may be sufficient to characterize the behavior(s) of interest [23, 80]. However, the efficiency of modern accelerometer sensors mean that little is gained, in terms of battery life, by using fewer axes.

The majority of studies in the literature sampled acceleration continuously, at frequencies above 1 Hz [97]. This type of sampling produces an extremely high volume of data; because each accelerometer axis is separate, three axes recording at 1 Hz produce three measurements per second, which rapidly accumulate into millions of logged measurements for a tag deployed over several days. In practice, continuous data are typically sub-sampled or averaged over several seconds’ worth of measurements to create a running mean [111] so an alternative to continuous sampling is to record for a few seconds at intervals of one or more minutes. By recording at high resolution (e.g., 60 Hz) but short duration (e.g., 1 to 3 sec) this strategy aims to sample just one behavior type and avoid behavioral transitions (e.g., resting to walking) that could complicate automated classification statistics. Each discrete sampling period is then called a “burst” or an “epoch” [22, 158]. Because burst studies record fewer data over the entire study period it is possible to download the data remotely through wireless connections [21, 40], whereas continuous accelerometer data typically are logged over days or weeks and manually downloaded upon tag retrieval [143]. If animals are expected to remain within the vicinity of a fixed receiver, then continuous data may be transmitted wirelessly at intervals [161]. If proximity to a receiver is problematic, as with marine animals that can range over very long distances, data can still be collected at high resolution (high sampling frequency and continuous sampling interval) as long as the entire sampling period matches device storage capacity, or there is periodic offloading of data via mobile receivers such as satellites.

Generally, the smaller the subject, the faster the movement and the higher the sampling frequency necessary to accurately characterize the pattern of acceleration [98, 123, 137]. From signal processing theory we have the rule-of-thumb that for adequate reconstruction of a continuous waveform such as acceleration, the sampling frequency ought to be at least twice that of the highest frequency movement being classified [162]. Sato et al. [123] measured the dominant stroke frequencies for several species of aquatic birds and marine mammals and they ranged from 0.2 Hz for sperm whales to 9.3 Hz in guillemots. Meanwhile, the three most common sampling frequencies in the literature were 10, 16, and 32 Hz but there was little a priori justification for the choice of sampling frequency. Halsey et al. found that accelerometer-sampling frequencies of 2 to 10 Hz were adequate for characterizing energy expenditure in chickens [19]. These studies suggest that sampling frequencies higher than 50 to 60 Hz are probably unnecessary for most research questions and that in such cases the additional data generated is wasteful of digital storage space. However, authors recommended that the research question and desired tempo-spatial resolution of the data should ultimately dictate the sampling frequency (and sampling interval) [19].

Best practices in accelerometer data analysis: describing the waveforms

For both continuous and burst sampling schemes, irrespective of sampling frequency, acceleration sensors produce raw data in a wave-like signal with units in voltage. Prior to analysis, researchers may use calibration equations to convert this signal into actual acceleration measured in m/s2 or g units where 1 g = 9.80665 m/s2 [57, 127, 154, 159]. This calibration and conversion may be required for measuring the actual biomechanical forces experienced by animals during different movements, for example, the air to water transition for a diving seabird, or the strike force on horses’ hooves when running. The dynamic body acceleration metrics also use the signal converted to acceleration as the proxy for metabolic exertion [96]. Alternatively, for simple acceleration ethograms or determining activity budgets, the signal may be used in the raw voltage state as depicted in Figure 6.

To describe the acceleration waveform patterns, researchers then calculate a wide variety of summary statistics using each burst’s population of values or subsamples of the continuous measurements (Table 1). The statistics listed in Table 1 could be calculated for each axis individually or combined to represent multiple axes simultaneously [149].
Table 1

Statistics used to describe acceleration waveforms

Summary statistic

Representative source(s)

Mean

[20, 23, 38]

Running mean for continuous data

[107, 111]

Minimum, Maximum, Range

[125, 130, 163]

Variance

[23, 36, 112]

Standard deviation

Inverse coefficient of variation

[62, 112]

Resultant

Overall dynamic body acceleration

[79, 103]

Vector dynamic body acceleration

[112]

Subsequent-measurement autocorrelation

[62]

Trend (linear regression coefficient through axis data)

Pair-wise correlations between axes data

Inclination, azimuth of resultant and their circular variances

Frequency power spectrum

Fast Fourier transformation

[43, 48, 57, 126, 129]

Continuous wavelet transformation

[164]

Δ acceleration

[20]

Δ frequency

Waveform frequency

[43, 130]

Waveform period and amplitude

[32, 164]

Area under the waveform curve

[163]

Skewness and kurtosis of the waveform

[165]

Signal magnitude area

[166]

Waveform length

Waveform inheritance

There is a dichotomy in the literature on how researchers process and describe the wave-like properties of accelerometer output. Some researchers have utilized simple waveform statistics, such as the number of peaks (frequency of the movement), the mean value of the waveform (body angle), and their variances [23, 35, 121, 131, 146, 160]. Others have used specialized programs to perform complex analyses on the waveforms, resulting in a large number of additional descriptive statistics [34, 46, 55, 127, 130, 144, 156, 161],[165]. There are numerous complex techniques for analyzing data that, like acceleration, exist in a time series [167, 168]. The most commonly used method with accelerometer data is the fast Fourier transformation. Fourier transformations identify the individual frequencies that are present in the raw acceleration waveform and determine the power spectral densities of those frequencies, i.e., how much of the total signal is present in each frequency [162]. Another complex approach is continuous wavelet transformation, which identifies not only which frequencies are present, but also when during the signal they are present [164]. Shepard et al. [20] and Laich et al. [23] suggest that these complex analyses are not essential and that simpler statistics are both intuitively and practically more accessible for the broadest range of potential users. However, they acknowledged that when behaviors “are transitory and/or highly variable” p. 36 [23], or are measured using only one axis of acceleration, the more complicated techniques and the additional statistics they provide may prove helpful for identifying or distinguishing different behaviors.

Data filters for separating gravitational acceleration from inertial acceleration

Recall that the accelerometer waveform output during movement is a combination of acceleration due to gravity and inertial acceleration due to animal movement (dynamic acceleration). When isolated, gravitational acceleration can be used to determine the orientation of the body in space (posture or body angle) [23, 55, 57]. The gravitational component can be isolated by: i) applying a low-pass filter such as 0.1 Hz that removes high frequency acceleration [41, 78, 79, 121, 164]; or ii) by smoothing (i.e., calculating a running mean) over a large set of measurements [57]. For acceleration sampled over a few seconds in a burst, taking the mean value of a single burst’s measurements can suffice for isolating momentary gravitational acceleration, or body angle [21]. One can see how this works in Figure 5; note the relatively flat slope of voltage output for all three axes when the animal is more or less motionless (left side designated ‘resting’). Between minutes 2 and 4, the mean value of the heave axis shifts dramatically as the animal changed position during rest from a ‘feet-up’ posture to a ‘feet-down’ posture. The change in the mean value of the heave axis represents a change in voltage output stimulated by gravitational acceleration after the tag (the animal) changed orientation. Determining exact body angle requires calibration of accelerometer voltage output as the tag is passed through 360 degrees along each axis. Using this method, researchers calculated body “pitch” angle from the heave or surge axes and body “roll” angle from the sway axis, also correcting for the position of the tag on the animal [41, 163].

Conversely, researchers used high-pass filters to examine accelerations due to movement in isolation from the gravitational component of acceleration [63, 159]. This dynamic component was used to calculate the measures of dynamic body acceleration in the majority of the studies on energetics [19, 20, 24, 77, 97, 107, 109, 111],[142, 159]. Frequency filters were also used to reduce the noise in the acceleration signal created by non-rigid attachments of accelerometer collars [31] and to isolate the pattern of one particular type of dynamic behavior that occurred simultaneously with other movements, namely prey capture events during swimming [155, 156]. Spectral and other waveform analyses discussed in the previous section are often conducted on dynamic acceleration after its isolation by high-pass frequency filtering.

Best practices in accelerometer data analysis: validation and assigning characteristic waveforms to behavior

The advantage of accelerometers is that they provide a remotely collected record of behavior: large sets of acceleration waveforms that were mostly not observed by the human eye. To understand how the acceleration record and the statistical properties of the waveforms relate to observable behavior, researchers using this tool must have a way of assigning the waveforms to specific behaviors or behavioral categories with a high degree of accuracy (validation). This task requires some prior knowledge of the behaviors animals perform and studies, to date, have generally obtained this information from deployments on similar domesticated animals, captive individuals and brief periods of observation on free-ranging wild animals, some via video [36]. Carefully synchronized observations and accelerometer recordings validate what behaviors correspond to what accelerometer measurements, for example, the relatively flat waveforms that occur during rest compared to the variable waveforms that occur during activity (Figure 5). This process also must quantify to what extent accelerometer waveforms for the same behavior vary within an individual, or between individuals or species [19, 109]. This validation process is a critically important part of using accelerometers. The accuracy of the conclusions drawn from assigning behaviors or energy expenditures to accelerometer waveforms depend enormously on the accuracy of the assignments (see discussion below on methods reporting).

This fact notwithstanding, wild animals, particularly aquatic species, may not be observed at all between release and tag recovery [42]. Even when animals are being observed directly, it is hard to be certain that all possible relevant behaviors have been witnessed [169], especially when extrapolating behavior in captivity to behavior in the wild. As accelerometry has matured, researchers have developed special software tools to address this obstacle and reduce the time and labor necessary for direct observation [164, 170]. With knowledge of i) general body shape, ii) form of locomotion (bipedal, quadrupedal, etc.), and iii) how the tag is attached to the body, these software programs can help researchers visualize the movement of their study animals according to the accelerometer signals recorded during tag deployment [170].

Manual examination of accelerometer data is essential in the pilot phases of a study, but an automatic system to categorize waveform patterns and assign them to different behaviors quickly becomes necessary due to the large size of the acceleration data sets. After calculating waveform characteristics such as those listed in Table 1, there are two major approaches to automatic waveform classification in the literature. The first is to use statistical algorithms to cluster accelerometer waveforms with similar characteristics and then assign each cluster to a general behavioral group [163]. For example, Sakamoto et al. [164] used an unsupervised k-means clustering algorithm to assign accelerometer waveforms from a diving seabird to 20 different groups, which they matched to simultaneously recorded depth profiles and then labeled with different behavior groups including ‘in flight’, ‘underwater diving’ and ‘on land’. The second, more common approach is to use the accelerometer waveforms generated from known (observed) behaviors of similar domesticated or captive individuals to train an algorithm that will assign the remaining waveforms in the dataset to those specific behavioral categories. For example, Nathan et al. [56] observed wild and captive vultures exhibit a variety of behaviors while wearing accelerometer tags and then used various supervised statistical algorithms to categorize the accelerometer waveforms as either active flight, passive flight (soaring-gliding), eating, lying down, preening, standing or running. Both methods lessen the burdens of extended direct observations and manual analysis of accelerometer data, however, the former has the potential to detect previously unknown or unobserved behaviors and behavioral sequences while the latter has the advantage that behavioral categories correspond directly to observations. Both rely on the validation process for accurate conclusions. Table 2 summarizes the assignment methods and algorithms represented in the literature; Nathan et al. [56] reviews and compares several of the supervised algorithms in detail.
Table 2

Methods for assigning accelerometer waveforms to behavioral categories based on waveform statistics

Method

Representative source(s)

Manual

  
 

Reference patterning

[34, 98, 108, 127, 171]

 

Fixed-threshold

[31, 57, 83, 161]

Unsupervised machine learning algorithms

 
 

Cluster analysis

[164]

Supervised machine learning algorithms

 
 

Classification and regression trees

[24, 77, 172]

 

Random forests

[55]

 

Linear or quadratic discriminant analysis

[55, 132, 149]

 

Logistic regression

[55, 148]

 

Support vector machines

[162, 172]

 

Artificial neural networks

[55, 172]

Regardless of the method used to assign accelerometer waveforms to behaviors, each group of researchers develops its own set of waveform statistics to feed to what are largely custom-designed automatic classification systems. Algorithms that deal with one particular domain of activities (e.g., diving in aquatic animals) may not be easily adapted for a different environment or different set of movements [32]. Furthermore, 58% of scientists working with domestic species and 47.6% of those working with captive wild species reported the performance and reliability of their chosen automated classification systems [55, 72], while only 9.1% of those studying wild species did so [23, 44, 57, 83]. Lack of methods reporting, from the accelerometer-recording schedule to whether and how accelerometer data is validated, stymies direct comparisons between studies and between analytical approaches. As previously discussed, validation is an essential part of using accelerometer as a stand-in for direct observation. If accelerometers are to achieve widespread use in studies of free-ranging wild species, there will have to be more complete reporting of methods, particularly for the classification phase of analysis.

Best practices in accelerometer data analysis: data visualization and storage

We noted earlier that the high resolution of accelerometry results in a large volume of data accumulating over a short period. For example, a single-axis accelerometer tag recording continuously at 8 Hz for 8 hours and 40 minutes resulted in 249,988 measurements [170]. When combined with the data from sensors deployed in tandem with tri-axial accelerometry, such as GPS, depth, or temperature telemeters, the dataset in its entirety can easily overwhelm basic spreadsheet and statistical programs and it becomes difficult to visualize more than one data stream at a time. ‘Igor Pro’ (WaveMetrics, Lake Oswego, OR, USA), ‘R’ (R Foundation for Statistical Computing, Vienna, Austria) and ‘Matlab’ (MathWorks, Natick, MA, USA) are commonly used for acceleration data analysis and can handle large datasets [83, 164, 165], although all three programs have a considerable learning curve. We are aware of two web-based options geared towards animal-borne telemeter data visualization, storage and analysis. MOVEBANK (available at https://www.movebank.org/) is a free, online database of animal tracking data that helps researchers to manage, selectively share, protect, analyze and archive their data. With MOVEBANK, researchers can link animal behavior from accelerometer data with animal location data from GPS and information from global environmental datasets, such as weather models and satellite imagery, making it easier to explore how animals’ movements relate to their environment. Gao et al. presented another online accelerometer data storage and analysis system, the Semantic Annotation and Activity Recognition system [166]. Their interactive web interface enables ecologists to visualize and correlate tri-axial accelerometer data streams while also facilitating accelerometer data analysis with a support vector machine classification algorithm. A major benefit to using these web-based repositories is that the average biologist tracking a handful of animals gets access to collaborations with other biologists, statisticians, engineers and computer programmers who can collectively continue to develop this tool and the hardware and software that make the most of accelerometry’s potential.

Conclusions and future directions

Accelerometry is a tool for fine-scale observations of behavior, unlimited by animal visibility, terrain, climate, observer bias or the scale of space use. To date, accelerometer tags have been applied to more than 120 species in diverse taxa in order to deduce body postures, behaviors and energetics in the field. Accelerometry also shows potential as a method to ‘dead reckon’ an animal’s exact travel path when applied in tandem with satellite-based location systems. In all of the research described, accelerometry provided fine-scale behavioral measurements that, prior to its development, were rarely attainable outside of the laboratory setting and without the influence of the researchers’ presence.

The literature shows several taxonomic biases in what research questions have been examined and how the results have been reported. Studies of wild aquatic species outnumber studies of wild terrestrial species. Research on aquatic animals (whether captive or wild) has focused on describing the biomechanics and energetic consequences of behavior, while in terrestrial systems the focus was on determining activity budgets. Both at sea and on land, feeding, locomotion, and activity/rest were the behavior categories most frequently analyzed; social behaviors (parental care, territorial, mating and courtship behaviors, and antagonistic exchanges) are nearly absent from both ethograms and energy budgets. Researchers of domestic and captive species tended to report analysis methods more thoroughly than those studying species in the wild.

There are substantial challenges to getting the most out of accelerometer data, including device retrieval and data calibration, validation, management and analysis. Numerous techniques for addressing these challenges have already been published in both human and animal studies and new methods continue to develop and are awaiting broad application to the field. With more thorough reporting of methodology and habitual use of web-based data repositories, universal practices are bound to emerge as hardware and software continues to mature and become more broadly available across research groups.

Future directions

The rapid development of this tool in the field, thus far, leads us to anticipate two promising breakthrough applications that will open even more doors in behavioral research. The first is the incorporation of inter-individual telemeters in the study of social behavior. Device-to-device data sharing and proximity sensors exist in the consumer electronics industry and have already been incorporated into acoustic telemeters [86]. Most modern telemetry tags have multiple data channels and could be modified to include these features. Accelerometer tags that have the ability to record proximity, identity and even behavior of tagged individuals in contact with the animal that bears the primary tag, would reduce the burden of extensive direct observation, yet permit scientists to directly question how individuals interact and how those interactions shape behavior across a large number of social and territorial species. Secondly, we recommend that researchers entering the field of wildlife telemetry look to explicitly link energetic expenditure in wild animals with behavioral responses to human-altered habitats [163]. Whether considering climate change, resource competition or anti-predator defenses, the potential toll on fitness should manifest in energetic expenditure and allow a window onto the longer-term consequences of our impacts on other long-lived species. Accelerometry offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and it has proven itself in furthering our understanding of the role of behavioral mechanisms in ecological and evolutionary processes.

Author’s contributions

DB collected the articles used in this review, carried out the data analysis, and drafted the manuscript and Figures 1 to 5. RK initially conceived of the idea to write a review of the subject and heavily influenced the organization of the information. MW, RW, and APK made critical revisions to drafts for comprehensiveness and presentation of the information. All authors read and approved the final manuscript.

Declarations

Acknowledgements

This review was supported by the New York State Museum; the Max Planck Institute for Ornithology; Swansea University; an NSF predoctoral fellowship (DD Brown) and the Biotelemetry Laboratory at the University of California, Davis. We thank Sebastián Cruz for contributing Figure 6 and Lewis Halsey for permission to reuse his figure (Figure 7) as well as helpful comments on an earlier draft.

Authors’ Affiliations

(1)
Department of Biology, Western Kentucky University
(2)
North Carolina Museum of Natural Sciences
(3)
Fisheries, Wildlife & Conservation, North Carolina State University
(4)
Smithsonian Tropical Research Institute
(5)
Max Planck Institute for Ornithology, Vogelwarte Radolfzell
(6)
Chair of Ornithology, Konstanz University
(7)
Biosciences, College of Science, Swansea University
(8)
Department of Wildlife, Fish, & Conservation Biology, 1334 Academic Surge, University of California

References

  1. Schneirla TC: The relationship between observation and experimentation in the field study of behavior. Annals NY Acad Sci 1950, 51: 1022–1044. 10.1111/j.1749-6632.1950.tb27331.xGoogle Scholar
  2. Carpenter CR: A field study of the behavioral and social relations of howling monkeys ( Alouatta palliata ). Comp Psychol Monographs 1934, 10: 1–168.Google Scholar
  3. Caro TM: Demography and behaviour of African mammals subject to exploitation. Biol Conserv 1999, 91: 91–97. 10.1016/S0006-3207(99)00033-6Google Scholar
  4. Maffei L, Noss AJ, Cuéllar E, Rumiz DI: Ocelot ( Felis pardalis ) population densities, activity, and ranging behaviour in the dry forests of eastern Bolivia: data from camera trapping. J Trop Ecol 2005, 21: 349–353. 10.1017/S0266467405002397Google Scholar
  5. Chapman FM: Who treads our trails. Nat Geogr Mag 1927, 52: 341–345.Google Scholar
  6. Jack KM, Lenz BB, Healan E, Rudman S, Schoof VAM, Fedigan L: The effects of observer presence on the behavior of Cebus capucinus in Costa Rica. Am J Primatol 2008, 70: 490–494. 10.1002/ajp.20512PubMedGoogle Scholar
  7. Crofoot MC, Lambert TD, Kays R, Wikelski MC: Does watching a monkey change its behaviour? Quantifying observer effects in habitutated wild primates using automated radiotelemetry. Anim Behav 2010, 80: 475–480. 10.1016/j.anbehav.2010.06.006Google Scholar
  8. Isbell LA, Young TP: Human presence reduces predation in a free-ranging vervet monkey population in Kenya. Anim Behav 1993, 45: 1233–1235. 10.1006/anbe.1993.1145Google Scholar
  9. Caine NG: Unrecognized anti-predator behavior can bias observational data. Anim Behav 1990, 39: 195–197. 10.1016/S0003-3472(05)80741-9Google Scholar
  10. Martin P, Bateson PPG: Measuring Behaviour. 3rd edition. Cambridge: Cambridge University Press; 2007.Google Scholar
  11. Cagnacci F, Boitani L, Powell RA, Boyce MS: Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Phil Transac Royal Soc B-Biol Sci 2010, 365: 2157–2162. 10.1098/rstb.2010.0107Google Scholar
  12. Kooyman GL: Genesis and evolution of bio-logging devices: 1963–2002. Memoirs Nat Inst Polar Res Special Issue 2004, 58: 15–22.Google Scholar
  13. Cooke SJ, Hinch SG, Wikelski M, Andrews RD, Kuchel LJ, Wolcott TG, Butler PJ: Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol 2004, 19: 334–343. 10.1016/j.tree.2004.04.003PubMedGoogle Scholar
  14. Altmann J: Observational study of behavior: sampling methods. Behaviour 1974, 49: 227–267. 10.1163/156853974X00534PubMedGoogle Scholar
  15. Hart KM, Hyrenbach KD: Satellite telemetry of marine megavertebrates: the coming of age of an experimental science. Endangered Spec Res 2009, 10: 9–20.Google Scholar
  16. Tomkiewicz SM, Fuller MR, Kie JG, Bates KK: Global positioning system and associated technologies in animal behaviour and ecological research. Philosophical Transac Royal Soc B-Biol Sci 2010, 365: 2163–2176. 10.1098/rstb.2010.0090Google Scholar
  17. Fryxell JM, Hazell M, Borger L, Dalziel BD, Haydon DT, Morales JM, McIntosh T, Rosatte RC: Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proc Natl Acad Sci USA 2008, 105: 19114–19119. 10.1073/pnas.0801737105PubMed CentralPubMedGoogle Scholar
  18. Aguiar LM, Moro-Rios RF: The direct observational method and possibilities for Neotropical Carnivores: an invitation for the rescue of a classical method spread over the Primatology. Zoologia 2009, 26: 587–593.Google Scholar
  19. Halsey LG, Green JA, Wilson RP, Frappell PB: Accelerometry to estimate energy expenditure during activity: best practice with data loggers. Physiol Biochem Zool 2009, 82: 396–404. 10.1086/589815PubMedGoogle Scholar
  20. Shepard ELC, Wilson RP, Quintana F, Gomez Laich A, Liebsch N, Albareda DA, Halsey LG, Gleiss A, Morgan DT, Myers AE, Newman C, Macdonald DW: Identification of animal movement patterns using tri-axial accelerometry. Endangered Spec Res 2010, 10: 47–60.Google Scholar
  21. Brown DD, LaPoint S, Kays R, Heidrich W, Kümmeth F, Wikelski M: Accelerometer-informed GPS telemetry: reducing the trade-Off between resolution and longevity. Wildlife Soc Bull 2012, 36: 139–146. 10.1002/wsb.111Google Scholar
  22. Dow C, Michel KE, Love M, Brown DC: Evaluation of optimal sampling interval for activity monitoring in companion dogs. Am J Vet Res 2009, 70: 444–448. 10.2460/ajvr.70.4.444PubMed CentralPubMedGoogle Scholar
  23. Laich AG, Wilson RP, Quintana F, Shepard ELC: Identification of imperial cormorant Phalacrocorax atriceps behaviour using accelerometers. Endangered Spec Res 2010, 10: 29–37.Google Scholar
  24. Wilson RP, Shepard ELC, Liebsch N: Prying into the intimate details of animal lives: use of a daily diary on animals. Endangered Spec Res 2008, 4: 123–137.Google Scholar
  25. Morris JRW: Accelerometry – A technique for the measurement of human body movements. J Biomech 1973, 6: 729–736. 10.1016/0021-9290(73)90029-8PubMedGoogle Scholar
  26. Chen KY, Bassett DR: The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exer 2005, 37: S490-S500. 10.1249/01.mss.0000185571.49104.82Google Scholar
  27. Servais SB, Webster JG, Montoye HJ: Estimating human energy expenditure using an accelerometer device. J Clin Eng 1984, 92: 159–171.Google Scholar
  28. Meijer GA, Westerterp KR, Koper H, Hoor FT: Assessment of energy expenditure by recording heart rate and body acceleration. Med Sci Sports Exerc 1989, 21: 343–347.PubMedGoogle Scholar
  29. Cavagna G, Saibene F, Margaria R: A three-directional accelerometer for analyzing body movements. J Appl Physiol 1961, 16: 191.PubMedGoogle Scholar
  30. Dubois AB, Cavagna GA, Fox RS: Locomotion of bluefish. J Exp Zool 1976, 195: 223–235. 10.1002/jez.1401950207PubMedGoogle Scholar
  31. Sellers WI, Crompton RH: Automatic monitoring of primate locomotor behaviour using accelerometers. Folia Primatologia 2004, 75: 279–293. 10.1159/000078939Google Scholar
  32. Mathie MJ, Celler BG, Lovell NH, Coster ACF: Classification of basic daily movements using a triaxial accelerometer. Med Biol Engineer Comp 2004, 42: 679–687. 10.1007/BF02347551Google Scholar
  33. Scheibe KM, Schleusner T, Berger A, Eichhorn K, Langbein J, Zotto LD, Streich WJ: ETHOSYS (R)–new system for recording and analysis of behaviour of free-ranging domestic animals and wildlife. Appl Anim Behav Sci 1998, 55: 195–211. 10.1016/S0168-1591(97)00072-5Google Scholar
  34. Sellers WI, Varley JS, Waters SS: Remote monitoring of locomotion using accelerometers: a pilot study. Folia Primatol 1998, 69: 82–85. 10.1159/000052700Google Scholar
  35. Yoda K, Sato K, Niizuma Y, Kurita M, Bost CA, Maho YL, Naito Y: Precise monitoring of porpoising behaviour of Adélie penguins determined using acceleration data loggers. J Exp Biol 1999, 202: 3121–3126.PubMedGoogle Scholar
  36. Davis RW, Fuiman LA, Williams TM, Collier SO, Hagey WP, Kanatous SB, Kohin S, Horning M: Hunting behavior of a marine mammal beneath the Antarctic fast ice. Science 1999, 283: 993–996. 10.1126/science.283.5404.993PubMedGoogle Scholar
  37. Kemp B, Janssen AJMW, Van Der Kamp B: Body position can be monitored in 3D using miniature accelerometers and earth-magnetic field sensors. Electroencephalogr Clin Neurophysiol 1998, 109: 484–488. 10.1016/S0924-980X(98)00053-8PubMedGoogle Scholar
  38. Holland RA, Wikelski M, Kümmeth F, Bosque C: The secret life of oilbirds: New insights into the movement ecology of a unique avian frugivore. PLoS ONE 2009, 4: e8264. 10.1371/journal.pone.0008264PubMed CentralPubMedGoogle Scholar
  39. Johnson MP, Tyack PL: A digital acoustic recording tag for measuring the response of wild marine mammals to sound. IEEE J Oceanic Engineer 2003, 28: 3–12. 10.1109/JOE.2002.808212Google Scholar
  40. Murchie KJ, Cooke SJ, Danylchuk AJ, Suski CD: Estimates of field activity and metabolic rates of bonefish (Albula vulpes) in coastal marine habitats using acoustic tri-axial accelerometer transmitters and intermittent-flow respirometry. J Experiment Marine Biol Ecol 2011, 396: 147–155. 10.1016/j.jembe.2010.10.019Google Scholar
  41. Sato K, Mitani Y, Cameron MF, Siniff DB, Naito Y: Factors affecting stroking patterns and body angle in diving Weddell seals under natural conditions. J Exp Biol 2003, 206: 1461–1470. 10.1242/jeb.00265PubMedGoogle Scholar
  42. Mitani Y, Andrews RD, Sato K, Kato A, Naito Y, Costa DP: Three-dimensional resting behaviour of northern elephant seals: drifting like a falling leaf. Biol Lett 2010, 6: 163–166. 10.1098/rsbl.2009.0719PubMed CentralPubMedGoogle Scholar
  43. van Oort BEH, Tyler NJC, Storeheier PV, Stokkan K-A: The performance and validation of a data logger for long-term determination of activity in free-ranging reindeer, Rangifer tarandus L. App Anim Behav Sci 2004, 89: 299–308. 10.1016/j.applanim.2004.06.009Google Scholar
  44. Gervasi V, Brunberg S, Swenson JE: An individual-based method to measure animal activity levels: a test on Brown Bears. Wildlife Soc Bull 2006, 34: 1314–1319. 10.2193/0091-7648(2006)34[1314:AIMTMA]2.0.CO;2Google Scholar
  45. Sakamoto Y, Kunisaki T, Sawaguchi I, Aoi T, Harashina K, Deguchi Y: A note on daily movement patterns of a female Asiatic black bear (ursus thibetanus) in a suburban area of Iwate prefecture, northeastern Japan. Mammal Study 2009, 34: 165–170. 10.3106/041.034.0306Google Scholar
  46. Whitney NM, Papastamatiou YP, Holland KN, Lowe CG: Use of an acceleration data logger to measure diel activity patterns in captive whitetip reef sharks, Triaenodon obesus. Aquatic Living Res 2007, 20: 299–305. 10.1051/alr:2008006Google Scholar
  47. Weimerskirch H, Shaffer SA, Tremblay Y, Costa DP, Gadenne H, Kato A, Ropert-Coudert Y, Sato K, Aurioles D: Species- and sex-specific differences in foraging behaviour and foraging zones in blue-footed and brown boobies in the Gulf of California. Marine Ecol Prog Ser 2009, 391: 267–278.Google Scholar
  48. Laich AG, Quintana F, Shepard ELC, Wilson RP: Intersexual differences in the diving behaviour of imperial cormorants. J Ornithol 2012, 153: 139–147. 10.1007/s10336-011-0714-1Google Scholar
  49. Zimmer I, Ropert-Coudert Y, Kato A, Ancel A, Chiaradia A: Does foraging performance change with Age in female little penguins (eudyptula minor)? PLoS ONE 2011,6(1):e16098. 10.1371/journal.pone.0016098PubMed CentralPubMedGoogle Scholar
  50. Byrnes G, Lim NTL, Yeong C, Spence AJ: Sex differences in the locomotor ecology of a gliding mammal, the Malayan colugo (Galeopterus variegatus). J Mammal 2011, 92: 444–451. 10.1644/10-MAMM-A-048.1Google Scholar
  51. Le Vaillant M, Wilson RP, Kato A, Saraux C, Hanuise N, Prud’Homme O, Le Maho Y, Le Bohec C, Ropert-Coudert Y: King penguins adjust their diving behaviour with age. J Experiment Biol 2012, 215: 3685–3692. 10.1242/jeb.071175Google Scholar
  52. Fossette S, Schofield G, Lilley MKS, Gleiss AC, Hays GC: Acceleration data reveal the energy management strategy of a marine ectotherm during reproduction. Functional Ecol 2012, 26: 324–333. 10.1111/j.1365-2435.2011.01960.xGoogle Scholar
  53. Kokubun N, Kim JH, Shin HC, Naito Y, Takahashi A: Penguin head movement detected using small accelerometers: a proxy of prey encounter rate. J Experiment Biol 2011, 214: 3760–3767. 10.1242/jeb.058263Google Scholar
  54. Viviant M, Trites AW, Rosen DAS, Monestiez P, Guinet C: Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers. Polar Biol 2010, 33: 713–719. 10.1007/s00300-009-0750-yGoogle Scholar
  55. Watanabe S, Izawa M, Kato A, Ropert-Coudert Y, Naito Y: A new technique for monitoring the detailed behaviour of terrestrial animals: a case study with the domestic cat. App Anim Behav Sci 2005, 94: 117–131. 10.1016/j.applanim.2005.01.010Google Scholar
  56. Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz WM: Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. J Experiment Biol 2012, 215: 986–996. 10.1242/jeb.058602Google Scholar
  57. Yoda K, Naito Y, Sato K, Takahashi A, Nishikawa J, Ropert-Coudert Y, Kurita M, Maho YL: A new technique for monitoring the behavior of free-ranging Adélie penguins. J Experiment Biol 2001, 204: 685–690.Google Scholar
  58. Kappeler PM, Erkert HG: On the move around the clock: correlates and determinants of cathemeral activity in wild redfronted lemurs ( Eulemur fulvus rufus ). Behavioral Ecol Sociobiol 2003, 54: 359–369. 10.1007/s00265-003-0652-xGoogle Scholar
  59. Erkert HG, Kappeler PM: Arrived in the light: diel and seasonal activity patterns in wild Verreaux’s sifakas (Propithecus v. verreaux; Primates: Indriidae). Behavioral Ecol Sociobiol 2004, 57: 174–186. 10.1007/s00265-004-0845-yGoogle Scholar
  60. Gilly WF, Zeidberg LD, Booth JAT, Stewart JS, Marshall G, Abernathy K, Bell LE: Locomotion and behavior of Humboldt squid, dosidicus gigas, in relation to natural hypoxia in the gulf of California, Mexico. J Experiment Biol 2012, 215: 3175–3190. 10.1242/jeb.072538Google Scholar
  61. Baras E, Togola B, Sicard B, Benech V: Behaviour of tigerfish Hydrocynus brevis in the River Niger, Mali, as revealed by simultaneous telemetry of activity and swimming depth. Hydrobiologia 2002, 483: 103–110. 10.1023/A:1021359008246Google Scholar
  62. Moreau M, Siebert S, Buerkert A, Schlecht E: Use of a tri-axial accelerometer for automated recording and classification of goats’ grazing behaviour. App Anim Behav Sci 2009, 119: 158–170. 10.1016/j.applanim.2009.04.008Google Scholar
  63. O’Toole AC, Murchie KJ, Pullen C, Hanson KC, Suski CD, Danylchuk AJ, Cooke SJ: Locomotory activity and depth distribution of adult great barracuda (Sphyraena barracuda) in Bahamian coastal habitats determined using acceleration and pressure biotelemetry transmitters. Marine Freshwater Res 2010, 61: 1446–1456. 10.1071/MF10046Google Scholar
  64. Wilson RP, Quintana F, Hobson VJ: Construction of energy landscapes can clarify the movement and distribution of foraging animals. Proc Roy Soc B-Biol Sci 2012, 279: 975–980. 10.1098/rspb.2011.1544Google Scholar
  65. Kays R, Jansen PA, Knecht EMH, Vohwinkel R, Wikelski M: The effect of feeding time on dispersal of Virola seeds by toucans determined from GPS tracking and accelerometers. Acta Oecologica-Int J Ecol 2011, 37: 625–631. 10.1016/j.actao.2011.06.007Google Scholar
  66. Ropert-Coudert Y, Gremillet D, Kato A, Ryan PG, Naito Y, Maho YL: A fine-scale time budget of Cape gannets provides insights into the foraging strategies of coastal seabirds. Anim Behav 2004, 67: 985–992. 10.1016/j.anbehav.2003.09.010Google Scholar
  67. Rothwell ES, Bercovitch FB, Andrews JRM, Anderson MJ: Estimating daily walking distance of captive African elephants using an accelerometer. Zoo Biol 2011, 30: 579–591. 10.1002/zoo.20364PubMedGoogle Scholar
  68. Takahashi M, Tobey JR, Pisacane CB, Andrus CH: Evaluating the utility of an accelerometer and urinary hormone analysis as indicators of estrus in a Zoo-housed koala ( phascolarctos cinereus ). Zoo Biol 2009, 28: 59–68. 10.1002/zoo.20212PubMedGoogle Scholar
  69. Thierman JL, Crowe TG, Stookey JM, Valentine B: Quantification of the response of elk during velvet antler removal. Can Agri Engineer 1999, 41: 223–237.Google Scholar
  70. Berger A, Scheibe K-M, Michaelis S, Streich WJ: Evaluation of living conditions of free-ranging animals by automated chronobiological analysis of behavior. Behav Rese Methods Instr Comp 2003, 35: 458–466. 10.3758/BF03195524Google Scholar
  71. Schaer BLD, Ryan CT, Boston RC, Nunamaker DM: The horse-racetrack interface: a preliminary study on the effect of shoeing on impact trauma using a novel wireless data acquisition system. Equine Vet J 2006, 38: 664–670.PubMedGoogle Scholar
  72. Cornou C, Lundbye-Christensen S: Classifying sows’ activity types from acceleration patterns an application of the multi-process kalman filter. App Anim Behav Sci 2008, 111: 262–273. 10.1016/j.applanim.2007.06.021Google Scholar
  73. White BJ, Coetzee JF, Renter DG, Babcock AH, Thomson DU, Andresen D: Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration. Am J Vet Res 2008, 69: 1005–1012. 10.2460/ajvr.69.8.1005PubMedGoogle Scholar
  74. Sullivan EL, Cameron JL: A rapidly occurring compensatory decrease in physical activity counteracts diet-induced weight loss in female monkeys. Am J Physiol Reg Integ Compar Physiol 2010,298(4):R1068-R1074. 10.1152/ajpregu.00617.2009Google Scholar
  75. Cooke SJ: Biotelemetry and biologging in endangered species research and animal conservation: relevance to regional, national, and IUCN Red List threat assessments. Endangered Spec Res 2008, 4: 165–185.Google Scholar
  76. Krone O, Berger A, Schulte R: Recording movement and activity pattern of a white-tailed Sea eagle (haliaeetus albicilla) by a GPS datalogger. J Ornithol 2009, 150: 273–280. 10.1007/s10336-008-0347-1Google Scholar
  77. Wilson RP, White CR, Quintana F, Halsey LG, Liebsch N, Martin GR, Butler PJ: Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant. J Anim Ecol 2006, 75: 1081–1090. 10.1111/j.1365-2656.2006.01127.xPubMedGoogle Scholar
  78. Tsuda Y, Kawabe R, Tanaka H, Mitsunaga Y, Hiraishi T, Yamamoto K, Nashimoto K: Monitoring the spawning behaviour of chum salmon with an acceleration data logger. Ecol Freshwater Fish 2006, 15: 264–274. 10.1111/j.1600-0633.2006.00147.xGoogle Scholar
  79. Yasuda T, Arai N: Changes in flipper beat frequency, body angle and swimming speed of female green turtles Chelonia mydas . Marine Ecol Progress Ser 2009, 386: 275–286.Google Scholar
  80. Zimmer I, Ropert-Coudert Y, Poulin N, Kato A, Chiaradia A: Evaluating the relative importance of intrinsic and extrinsic factors on the foraging activity of top predators: a case study on female little penguins. Marine Biol 2011, 158: 715–722. 10.1007/s00227-010-1594-2Google Scholar
  81. Jensen MB: Behaviour around the time of calving in dairy cows. App Anim Behav Sci 2012, 139: 195–202. 10.1016/j.applanim.2012.04.002Google Scholar
  82. Whitney NM, Harold LP Jr, Pratt TC, Carrier JC: Identifying shark mating behaviour using three-dimensional acceleration loggers. Endangered Spec Res 2010, 10: 71–82.Google Scholar
  83. Lagarde F, Guillon M, Dubroca L, Bonnet X, Ben Kaddour K, Slimani T, El Mouden EH: Slowness and acceleration: a new method to quantify the activity budget of chelonians. Anim Behav 2008, 75: 319–329. 10.1016/j.anbehav.2007.01.010Google Scholar
  84. Ismail A, Rahman F, Miyazaki N, Naito Y: Initial application of bio-logging techniques on captive Milky Stork (Mycteria cinerea) in Malaysia. Trop Ecol 2012, 53: 177–181.Google Scholar
  85. Rushen J, de Passille AM: Automated measurement of acceleration can detect effects of age, dehorning and weaning on locomotor play of calves. App Anim Behav Sci 2012, 139: 169–174. 10.1016/j.applanim.2012.04.011Google Scholar
  86. Holland KN, Meyer CG, Dagorn LC: Inter-animal telemetry: results from first deployment of acoustic ‘business card’ tags. Endangered Spec Res 2010, 10: 287–293.Google Scholar
  87. De Rouffignac C, Morel F: A comparative study of water turnover in 4 rodent species of which 2 are from the desert—Meriones-Shawi rat mouse gerbil. J de Physiologie (Paris) 1966, 58: 309–322.Google Scholar
  88. Arnould JPY, Boyd IL, Speakman JR: The relationship between foraging behaviour and energy expenditure in Antarctic fur seals. J Zool (London) 1996, 239: 769–782. 10.1111/j.1469-7998.1996.tb05477.xGoogle Scholar
  89. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB: Toward a metabolic theory of ecology. Ecology 2004, 85: 1771–1789. 10.1890/03-9000Google Scholar
  90. Altmann S, Altmann J: The transformation of behaviour field studies. Anim Behav 2003, 65: 413–423. 10.1006/anbe.2003.2115Google Scholar
  91. Speakman JR: Doubly Labelled Water: Theory and Practice. Cambridge: Cambridge University Press; 1997.Google Scholar
  92. Butler PJ, Green JA, Boyd IL, Speakman JR: Measuring metabolic rate in the field: the pros and cons of the doubly labelled water and heart rate methods. Functional Ecol 2004, 18: 168–183. 10.1111/j.0269-8463.2004.00821.xGoogle Scholar
  93. Ropert-Coudert Y, Wilson RP: Trends and perspectives in animal-attached remote sensing. Front Ecol Environ 2005, 3: 437–444. 10.1890/1540-9295(2005)003[0437:TAPIAR]2.0.CO;2Google Scholar
  94. Hindle AG, Rosen DAS, Trites AW: Swimming depth and ocean currents affect transit costs in Steller sea lions Eumetopias jubatus. Aqua Biol 2010, 10: 139–148. 10.3354/ab00279Google Scholar
  95. Fahlman A, Wilson R, Svard C, Rosen DAS, Trites AW: Activity and diving metabolism correlate in Steller sea lion Eumetopias jubatus. Aqua Biol 2008, 2: 75–84.Google Scholar
  96. Halsey LG, White CR, Enstipp MR, Wilson RP, Butler PJ, Martin GR, Gremillet D, Jones DR: Assessing the validity of the accelerometry technique for estimating the energy expenditure of diving double-crested cormorants phalacrocorax auritus. Physiol Biochem Zool 2011, 84: 230–237. 10.1086/658636PubMedGoogle Scholar
  97. Wilson RP, Shepard ELC, Laich AG, Frere E, Quintana F: Pedalling downhill and freewheeling up; a penguin perspective on foraging. Aqua Biol 2010, 8: 193–202.Google Scholar
  98. Shepard ELC, Wilson RP, Quintana F, Laich AGM, Forman DW: Pushed for time or saving on fuel: fine-scale energy budgets shed light on currencies in a diving bird. Proc R Soc Ser B Biol Sci 2009, 276: 3149–3155. 10.1098/rspb.2009.0683Google Scholar
  99. Shepard ELC, Wilson RP, Laich AG, Quintana F: Buoyed up and slowed down: speed limits for diving birds in shallow water. Aqua Biol 2010, 8: 259–267.Google Scholar
  100. Gleiss AC, Norman B, Wilson RP: Moved by that sinking feeling: variable diving geometry underlies movement strategies in whale sharks. Functional Ecol 2011, 25: 595–607. 10.1111/j.1365-2435.2010.01801.xGoogle Scholar
  101. Gleiss AC, Dale JJ, Holland KN, Wilson RP: Accelerating estimates of activity-specific metabolic rate in fishes: testing the applicability of acceleration data-loggers. J Experiment Marine Biol Ecol 2010, 385: 85–91. 10.1016/j.jembe.2010.01.012Google Scholar
  102. Fossette S, Gleiss AC, Myers AE, Garner S, Liebsch N, Whitney NM, Hays GC, Wilson RP, Lutcavage ME: Behaviour and buoyancy regulation in the deepest-diving reptile: the leatherback turtle. J Experiment Biol 2010, 213: 4074–4083. 10.1242/jeb.048207Google Scholar
  103. Enstipp MR, Ciccione S, Gineste B, Milbergue M, Ballorain K, Ropert-Coudert Y, Kato A, Plot V, Georges JY: Energy expenditure of freely swimming adult green turtles (Chelonia mydas) and its link with body acceleration. J Experiment Biol 2011, 214: 4010–4020. 10.1242/jeb.062943Google Scholar
  104. Halsey LG, Jones TT, Jones DR, Liebsch N, Booth DT: Measuring energy expenditure in Sub-adult and hatchling Sea turtles via accelerometry. PLoS ONE 2011, 6: e22311. 10.1371/journal.pone.0022311PubMed CentralPubMedGoogle Scholar
  105. Robson AA, Chauvaud L, Wilson RP, Halsey LG: Small actions, big costs: the behavioural energetics of a commercially important invertebrate. J R Soc Interface 2012, 9: 1486–1498. 10.1098/rsif.2011.0713PubMed CentralPubMedGoogle Scholar
  106. Halsey LG, Shepard ELC, Quintana F, Gomez Laich A, Green JA, Wilson RP: The relationship between oxygen consumption and body acceleration in a range of species. Compar Biochemi Physiol Part A Mole Integr Physiol 2009, 152: 197–202. 10.1016/j.cbpa.2008.09.021Google Scholar
  107. Green JA, Halsey LG, Wilson RP, Frappell PB: Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique. J Exp Biol 2009, 212: 471–482. 10.1242/jeb.026377PubMedGoogle Scholar
  108. Halsey LG, Portugal SJ, Smith JA, Murn CP, Wilson RP: Recording raptor behavior on the wing via accelerometry. J Field Ornithol 2009, 80: 171–177. 10.1111/j.1557-9263.2009.00219.xGoogle Scholar
  109. Halsey LG, White CR: Measuring energetics and behaviour using accelerometry in cane toads bufo marinus. PLoS ONE 2010,5(4):e10170. 10.1371/journal.pone.0010170PubMed CentralPubMedGoogle Scholar
  110. Halsey LG, Shepard ELC, Wilson RP: Assessing the development and application of the accelerometry technique for estimating energy expenditure. Compar Biochem Physiol Mole Integr Physiol 2011, 158: 305–314. 10.1016/j.cbpa.2010.09.002Google Scholar
  111. Shepard ELC, Wilson RP, Halsey LG, Quintana F, Gomez Laich A, Gleiss AC, Liebsch N, Myers AE, Norman B: Derivation of body motion via appropriate smoothing of acceleration data. Aqua Biol 2009, 4: 235–241.Google Scholar
  112. Qasem L, Cardew A, Wilson A, Griffiths I, Halsey LG, Shepard ELC, Gleiss AC, Wilson R: Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector? PLoS ONE 2012,7(2):e31187. 10.1371/journal.pone.0031187PubMed CentralPubMedGoogle Scholar
  113. Leleu C, Bariller F, Cotrel C, Barrey E: Reproducibility of a locomotor test for trotter horses. Vet J 2004, 168: 160–166. 10.1016/S1090-0233(03)00109-6PubMedGoogle Scholar
  114. Leleu C, Cotrel C, Barrey E: Relationships between biomechanical variables and race performance in French Standard bred trotters. Livestock Prod Sci 2005, 92: 39–46. 10.1016/j.livprodsci.2004.07.019Google Scholar
  115. Witte TH, Knill K, Wilson AM: Determination of peak vertical ground reaction force from duty factor in the horse (Equus caballus). J Experiment Biol 2004, 207: 3639–3648. 10.1242/jeb.01182Google Scholar
  116. Ratzlaff MH, Wilson PD, Hutton DV, Slinker BK: Relationships between hoof-acceleration patterns of galloping horses and dynamic properties of the track. Am J Vet Res 2005, 66: 589–595. 10.2460/ajvr.2005.66.589PubMedGoogle Scholar
  117. Cottin F, Metayer N, Goachet AG, Julliand V, Slawinski J, Billat V, Barrey E: Oxygen consumption and gait variables of Arabian endurance horses measured during a field exercise test. Equine Vet J 2010, 42: 1–5.Google Scholar
  118. Brischoux F, Kato A, Ropert-Coudert Y, Shine R: Swimming speed variation in amphibious seasnakes (laticaudinae): a search for underlying mechanisms. J Experiment Marine Biol Ecol 2010, 394: 116–122. 10.1016/j.jembe.2010.08.001Google Scholar
  119. Sato K, Sakamoto KQ, Watanuki Y, Takahashi A, Katsumata N, Bost C-A, Weimerskirch H: Scaling of soaring seabirds and implications for flight abilities of giant pterosaurs. PLoS ONE 2009,4(4):e5400. 10.1371/journal.pone.0005400PubMed CentralPubMedGoogle Scholar
  120. Hoyt DF, Wickler SJ, Cogger EA, Goehring ME: A reexamination of the trot-gallop transition: insights from the study of locomotion on an incline. Am Zool 2000, 40: 1066.Google Scholar
  121. Tanaka H, Takagi Y, Naito Y: Swimming speeds and buoyancy compensation of migrating adult chum salmon Oncorhynchus keta revealed by speed/depth/acceleration data logger. J Experiment Biol 2001, 204: 3895–3904.Google Scholar
  122. Ropert-Coudert Y, Kato A, Wilson RP, Cannell B: Foraging strategies and prey encounter rate of free-ranging Little Penguins. Marine Biol (Berlin) 2006, 149: 139–148. 10.1007/s00227-005-0188-xGoogle Scholar
  123. Sato K, Watanuki Y, Takahashi A, Miller PJO, Tanaka H, Kawabe R, Ponganis PJ, Handrich Y, Akamatsu T, Watanabe Y, Mitani Y, Costa DP, Bost CA, Aoki K, Amano M, Trathan P, Shapiro A, Naito Y: Stroke frequency, but not swimming speed, is related to body size in free-ranging seabirds, pinnipeds and cetaceans. Proc R Soc Series B Biol Sci 2007, 274: 471–477. 10.1098/rspb.2006.0005Google Scholar
  124. Hindle AG, Young BL, Rosen DAS, Haulena M, Trites AW: Dive response differs between shallow- and deep-diving Steller sea lions (Eumetopias jubatus). J Experiment Marine Biol Ecol 2010, 394: 141–148. 10.1016/j.jembe.2010.08.006Google Scholar
  125. Kato A, Ropert-Coudert Y, Gremillet D, Cannell B: Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Marine Ecol Prog Series 2006, 308: 293–301.Google Scholar
  126. Watanuki Y, Wanless S, Harris M, Lovvorn JR, Miyazaki M, Tanaka H, Sato K: Swim speeds and stroke patterns in wing-propelled divers: a comparison among alcids and a penguin. J Experiment Biol 2006, 209: 1217–1230. 10.1242/jeb.02128Google Scholar
  127. Kawabe R, Nashimoto K, Hiraishi T, Naito Y, Sato K: A new device for monitoring the activity of freely swimming flatfish, Japanese flounder Paralichthys olivaceus . Fisheries Sci 2003, 69: 3–10. 10.1046/j.1444-2906.2003.00581.xGoogle Scholar
  128. Williams TM, Davis RW, Fuiman LA, Francis J, Le Boeuf BL, Horning M, Calambokidis J, Croll DA: Sink or swim: strategies for cost-efficient diving by marine mammals. Science 2000, 288: 133–136. 10.1126/science.288.5463.133PubMedGoogle Scholar
  129. Goldbogen JA, Calambokidis J, Shadwick RE, Oleson EM, McDonald MA, Hildebrand JA: Kinematics of foraging dives and lunge-feeding in fin whales. J Experiment Biol 2006, 209: 1231–1244. 10.1242/jeb.02135Google Scholar
  130. Arai N, Kuroki M, Sakamoto W, Naito Y: Analysis of diving behavior of Adélie penguins using acceleration data logger. Polar Biosci 2000, 13: 95–100.Google Scholar
  131. Watanuki Y, Takahashi A, Daunt F, Wanless S, Harris M, Sato K, Naito Y: Regulation of stroke and glide in a foot-propelled avian diver. J Experiment Biol 2005, 208: 2207–2216. 10.1242/jeb.01639Google Scholar
  132. Soltis J, Wilson RP, Douglas-Hamilton I, Vollrath F, King LE, Savage A: Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana . Endangered Species Res 2012, 18: 255–263. 10.3354/esr00452Google Scholar
  133. Green JA, White CR, Bunce A, Frappell PB, Butler PJ: Energetic consequences of plunge diving in gannets. Endangered Species Res 2009, 10: 269–279.Google Scholar
  134. Fossette S, Gaspar P, Handrich Y, Le Maho Y, Georges J-Y: Dive and beak movement patterns in leatherback turtles Dermochelys coriacea during internesting intervals in French Guiana. J Anim Ecol 2008, 77: 236–246. 10.1111/j.1365-2656.2007.01344.xPubMedGoogle Scholar
  135. Gleiss AC, Wilson RP, Shepard ELC: Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Methods Ecol Evol 2011, 2: 23–33. 10.1111/j.2041-210X.2010.00057.xGoogle Scholar
  136. Kays R, Tilak S, Crofoot MC, Fountain T, Obando D, Ortega A, Kuemmeth F, Mandel J, Swenson G, Lambert T, Hirsch B, Wikelski M: Tracking animal location and activity with an automated radio telemetry system in a tropical rainforest. Comp J 2011,54(12):1931–1948. 10.1093/comjnl/bxr072Google Scholar
  137. Shiomi K, Sato K, Ponganis PJ: Point of no return in diving emperor penguins: is the timing of the decision to return limited by the number of strokes? J Experiment Biol 2012, 215: 135–140. 10.1242/jeb.064568Google Scholar
  138. Ropert-Coudert Y, Wilson RP: Subjectivity in bio-logging science: do logged data mislead? Memoirs Nat Inst Polar Res 2004, 58: 23–33.Google Scholar
  139. Bidder OR, Soresina M, Shepard EL, Halsey LG, Quintana F, Gomez Laich A, Wilson RP: The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems. Zoology 2012, 115: 58–64. 10.1016/j.zool.2011.09.003PubMedGoogle Scholar
  140. Tobler MW: New GPS technology improves fix success for large mammal collars in dense tropical forests. J Trop Ecol 2009, 25: 217–221. 10.1017/S0266467409005811Google Scholar
  141. Narazaki T, Sato K, Abernathy KJ, Marshall GJ, Miyazaki N: Sea turtles compensate deflection of heading at the sea surface during directional travel. J Experiment Biol 2009, 212: 4019–4026. 10.1242/jeb.034637Google Scholar
  142. Wilson RP, Liebsch N, Davies IM, Quintana F, Weimerskirch H, Storch S, Lucke K, Siebert U, Zankl S, Müller G, Zimmer I, Scolaro A, Campagna C, Plötz J, Bornemann H, Teilmann J, McMahon CR: All at sea with animal tracks; methodological and analytical solutions for the resolution of movement. Deep Sea Res Part II Topic Stud Oceanogr 2007, 54: 193–210. 10.1016/j.dsr2.2006.11.017Google Scholar
  143. Shiomi K, Sato K, Mitamura H, Arai N, Naito Y, Ponganis PJ: Effect of ocean current on the dead-reckoning estimation of 3-D dive paths of emperor penguins. Aqua Biol 2008, 3: 265–270.Google Scholar
  144. Shiomi K, Narazaki T, Sato K, Shimatani K, Arai N, Ponganis PJ, Miyazaki N: Data-processing artefacts in three-dimensional dive path reconstruction from geomagnetic and acceleration data. Aqua Biol 2010, 8: 299–304.Google Scholar
  145. Houghton JDR, Liebsch N, Doyle TK, Gleiss AC, Lilley MKS, Wilson RP, Hays GC: Harnessing the sun: testing a novel attachment method to record fine scale movements in ocean sunfish (mola mola). In Tagging and Tracking of Marine Animals with Electronic Devices. New York: Springer: Nielsen JL, Arrizabalaga H, Fragoso N, Hobday A, Lutcavage M, Sibert J (Eds.); 2009:229–242.Google Scholar
  146. de Passillé AM, Jensen MB, Chapinal N, Rushen J: Use of accelerometers to describe gait patterns in dairy calves. J Dairy Sci 2010, 93: 3287–3293. 10.3168/jds.2009-2758PubMedGoogle Scholar
  147. Gleiss AC, Norman B, Liebsch N, Francis C, Wilson RP: A new prospect for tagging large free-swimming sharks with motion-sensitive data-loggers. Fisheries Res (Amsterdam) 2009, 97: 11–16. 10.1016/j.fishres.2008.12.012Google Scholar
  148. Signer C, Ruf T, Schober F, Fluch G, Paumann T, Arnold W: A versatile telemetry system for continuous measurement of heart rate, body temperature and locomotor activity in free-ranging ruminants. Methods Ecol Evol 2010, 1: 75–85. 10.1111/j.2041-210X.2009.00010.xPubMed CentralPubMedGoogle Scholar
  149. Watanabe N, Sakanoue S, Kawamura K, Kozakai T: Development of an automatic classification system for eating, ruminating and resting behavior of cattle using an accelerometer. Grassland Sci 2008, 54: 231–237. 10.1111/j.1744-697X.2008.00126.xGoogle Scholar
  150. Ledgerwood DN, Winckler C, Tucker CB: Evaluation of data loggers, sampling intervals, and editing techniques for measuring the lying behavior of dairy cattle. J Dairy Sci 2010, 93: 5129–5139. 10.3168/jds.2009-2945PubMedGoogle Scholar
  151. Keegan KG, Kramer J, Yonezawa Y, Maki H, Pai PF, Dent EV, Kellerman TE, Wilson DA, Reed SK: Assessment of repeatability of a wireless, inertial sensor-based lameness evaluation system for horses. Am J Vet Res 2011, 72: 1156–1163. 10.2460/ajvr.72.9.1156PubMedGoogle Scholar
  152. Preston T, Baltzer W, Trost S: Accelerometer validity and placement for detection of changes in physical activity in dogs under controlled conditions on a treadmill. Res Vet Sci 2012, 93: 412–416. 10.1016/j.rvsc.2011.08.005PubMedGoogle Scholar
  153. Okuyama J, Kawabata Y, Naito Y, Arai N, Kobayashi M: Monitoring beak movements with an acceleration datalogger: a useful technique for assessing the feeding and breathing behaviors of sea turtles. Endangered Species Res 2009, 10: 39–45.Google Scholar
  154. Suzuki I, Naito Y, Folkow LP, Miyazaki N, Blix AS: Validation of a device for accurate timing of feeding events in marine animals. Polar Biol 2009, 32: 667–671. 10.1007/s00300-009-0596-3Google Scholar
  155. Skinner JP, Norberg SE, Andrews RD: Head striking during fish capture attempts by Steller sea lions and the potential for using head surge acceleration to predict feeding behavior. Endangered Species Res 2010, 10: 61–69.Google Scholar
  156. Iwata T, Sakamoto KQ, Takahashi A, Edwards EWJ, Staniland IJ, Trathan PN, Naito Y: Using a mandible accelerometer to study fine-scale foraging behavior of free-ranging Antarctic fur seals. Marine Mammal Sci 2012, 28: 345–357. 10.1111/j.1748-7692.2011.00482.xGoogle Scholar
  157. Sato K, Daunt F, Watanuki Y, Takahashi A, Wanless S: A new method to quantify prey acquisition in diving seabirds using wing stroke frequency. J Experiment Biol 2008, 211: 58–65. 10.1242/jeb.009811Google Scholar
  158. Robert B, White BJ, Renter DG, Larson RL: Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Comp Electron Agri 2009, 67: 80–84. 10.1016/j.compag.2009.03.002Google Scholar
  159. Halsey LG, Shepard ELC, Hulston CJ, Venables MC, White CR, Jeukendrup AE, Wilson RP: Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: tests with an easy model species, Homo sapiens. Zoology 2008, 111: 231–241. 10.1016/j.zool.2007.07.011PubMedGoogle Scholar
  160. Gleiss AC, Gruber SH, Wilson RP: Multi-channel data-logging: towards determination of behaviour and metabolic rate in free-swimming sharks. In Tagging and Tracking of Marine Animals with Electronic Devices. New York: Springer: Nielsen JL, Arrizabalaga H, Fragoso N, Hobday A, Lutcavage M, Sibert J; 2009:211–228.Google Scholar
  161. Scheibe KM, Gromann C: Application testing of a new three-dimensional acceleration measuring system with wireless data transfer (WAS) for behavior analysis. Behav Res Methods 2006, 38: 427–433. 10.3758/BF03192796PubMedGoogle Scholar
  162. Yost M, Cooper RA, Bremner FJ: Fourier analyses: a mathematical and geometric explanation. Behav Res Methods Inst 1983, 15: 258–261. 10.3758/BF03203558Google Scholar
  163. Watanabe S, Sato K, Ponganis PJ: Activity time budget during foraging trips of emperor penguins. PLoS ONE 2012,7(11):e50357. 10.1371/journal.pone.0050357PubMed CentralPubMedGoogle Scholar
  164. Sakamoto KQ, Sato K, Ishizuka M, Watanuki Y, Takahashi A, Daunt F, Wanless S: Can ethograms be automatically generated using body acceleration data from free-ranging birds? PLoS ONE 2009,4(4):e5379. 10.1371/journal.pone.0005379PubMed CentralPubMedGoogle Scholar
  165. Martiskainen P, Järvinen M, Skön J-P, Tiirikainen J, Kolehmainen M, Mononen J: Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. App Anim Behav Sci 2009, 119: 32–38. 10.1016/j.applanim.2009.03.005Google Scholar
  166. Gao L, CAmpbell HA, Bidder OR, Hunter J: A Web-based semantic tagging and activity recognition system for species’ accelerometry data. Ecological Info 2012, 13: 47–56.Google Scholar
  167. Kaplan HL: Correlations, contrasts, and components: Fourier analysis in a more familiar terminology. Behav Res Methods Inst 1983, 15: 228–241. 10.3758/BF03203554Google Scholar
  168. Hart T, Mann R, Coulson T, Pettorelli N, Trathan P: Behavioural switching in a central place forager: patterns of diving behaviour in the macaroni penguin (Eudyptes chrysolophus). Marine Biol 2010, 157: 1543–1553. 10.1007/s00227-010-1428-2Google Scholar
  169. Jule KR, Lea SEG, Leaver L: Using a behaviour discovery curve to predict optimal observation time. Behaviour 2009, 146: 1531–1542. 10.1163/156853909X447775Google Scholar
  170. Grundy E, Jones MW, Laramee RS, Wilson RP, Shepard ELC: Visualisation of sensor data from animal movement. IEEE-VGTC Symposium Visual 2009, 28: 815–822.Google Scholar
  171. Ropert-Coudert Y, Gremillet D, Ryan P, Kato A, Naito Y, Le Maho Y: Between air and water: the plunge dive of the cape gannet morus capensis. Ibis 2004, 146: 281–290.Google Scholar
  172. Nadimi ES, Sogaard HT, Bak T: ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosystems Engineer 2008, 100: 167–176. 10.1016/j.biosystemseng.2008.03.003Google Scholar

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