Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant

Respirometer in a box: development and use of a portable field respirometer for estimating oxygen consumption of large-bodied fishes

This study developed a portable, low-cost field respirometer for measuring oxygen consumption rates of large-bodied fishes. The respirometer performed well in laboratory tests and was used to measure the oxygen consumption rates ( M ˙ O₂ ) of bull sharks Carcharhinus leucas (mean: 249.21 ± 58.10 mg O₂ kg^-1 h^-1 at 27.05°C). Interspecific comparisons and assessments of oxygen degradation curves indicated that the respirometer provided reliable measurements of M ˙ O₂ . This system presents a field-based alternative to laboratory respirometers, opening opportunities for studies on species in remote localities, increasing the ability to validate physiological field studies.

Exploration profiles drive activity patterns and temporal niche specialization in a wild rodent

Individual niche specialization can have important consequences for competition, fitness, and, ultimately, population dynamics and ecological speciation. The temporal window and the level of daily activity are niche components that may vary with sex, breeding season, food supply, population density, and predator’s circadian rhythm. More recently, ecologists emphasized that traits such as dispersal and space use could depend on personality differences. Boldness and exploration have been shown to correlate with variation in foraging patterns, habitat use, and home range. Here, we assessed the link between exploration, measured from repeated novel environment tests, activity patterns, and temporal niche specialization in wild eastern chipmunks (Tamias striatus). Intrinsic differences in exploration should drive daily activity patterns through differences in energy requirements, space use, or the speed to access resources. We used collar-mounted accelerometers to assess whether individual exploration profiles predicted: 1) daily overall dynamic body acceleration, reflecting overall activity levels; 2) mean activity duration and the rate of activity sequences, reflecting the structure of daily activity; and 3) patterns of dawn and dusk activity, reflecting temporal niche differentiation. Exploration and overall activity levels were weakly related. However, both dawn activity and rate of activity sequences increased with the speed of exploration. Overall, activity patterns varied according to temporal variability in food conditions. This study emphasizes the role of intrinsic behavioral differences in activity patterns in a wild animal population. Future studies will help us understand how yearly seasonality in reproduction, food abundance, and population density modulate personality-dependent foraging patterns and temporal niche specialization.

From a calf's perspective: humpback whale nursing behavior on two US feeding grounds

Nursing influences growth rate and overall health of mammals; however, the behavior is difficult to study in wild cetaceans because it occurs below the surface and can thus be misidentified from surface observations. Nursing has been observed in humpback whales on the breeding and calving grounds, but the behavior remains unstudied on the feeding grounds. We instrumented three dependent calves (four total deployments) with combined video and 3D-accelerometer data loggers (CATS) on two United States feeding grounds to document nursing events. Two associated mothers were also tagged to determine if behavior diagnostic of nursing was evident in the mother’s movement. Animal-borne video was manually analyzed and the average duration of successful nursing events was 23 s (±7 sd, n = 11). Nursing occurred at depths between 4.1–64.4 m (along the seafloor) and in close temporal proximity to foraging events by the mothers, but could not be predicted solely by relative positions of mother and calf. When combining all calf deployments, successful nursing was documented eleven times; totaling only 0.3% of 21.0 hours of video. During nursing events, calves had higher overall dynamic body acceleration (ODBA) and increased fluke-stroke rate (FSR) compared to non-nursing segments (Mixed effect models, ODBA: F1,107 = 13.57756, p = 0.0004, FSR: F1,107 = 32.31018, p < 0.0001). In contrast, mothers had lower ODBA and reduced FSR during nursing events compared to non-nursing segments. These data provide the first characterization of accelerometer data of humpback whale nursing confirmed by animal-borne video tags and the first analysis of nursing events on feeding grounds. This is an important step in understanding the energetic consequences of lactation while foraging.

Heart rate and cardiac response to exercise during voluntary dives in captive sea turtles (Cheloniidae)

In chelonids, oxygen is primarily stored in the lungs during a dive. Therefore, management of blood oxygen transportation to peripheral tissues by cardiovascular adjustments during submergence is crucial to maximize their dive duration, and consequently, the time spent for ecological activities such as foraging. However, the cardiac response to exercise has rarely been examined in sea turtles. In this study, heart rate and its relationship with exercise during voluntary dives were determined in six captive green turtles (19.4±1.5 kg) by simultaneously recording depth, acceleration and electrocardiogram. Our results demonstrated that the heart rate of green turtles was generally low (11.1±0.4 bpm) during resting dives, but they often exhibited instantaneously extreme tachycardia (up to 78.4 bpm). Green turtles elevated their heart rate up to 39.8±1.5 bpm during ventilation after resting dives, while up to 33.1±1.4 bpm after active dives. The heart rate immediately elevated with onset of exercise, and increased linearly with exercise. This result may indicate that turtles immediately need to transport oxygen from the lungs to peripheral tissues by pulmonary and systemic circulations to meet the metabolic demands of exercise because they mainly store oxygen in their lungs.

Active acoustic telemetry tracking and tri-axial accelerometers reveal fine-scale movement strategies of a non-obligate ram ventilator

BACKGROUND

California horn sharks (Heterodontus francisci) are nocturnally active, non-obligate ram ventilating sharks in rocky reef habitats that play an important ecological role in regulating invertebrate communities. We predicted horn sharks would use an area restricted search (ARS) movement strategy to locate dense resource patches while minimizing energetic costs of travel and nighttime activity. As ectotherms, we predicted environmental temperature would play a significant role in driving movement and activity patterns.

METHODS

Continuous active acoustic tracking methods and acceleration data loggers were used to quantify the diel fine-scale spatial movements and activity patterns of horn sharks. First passage time was used to identify the scale and locations of patches indicative of ARS. Activity was assessed using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure. Behavior within a patch was characterized into three activity patterns: resting, episodic burst activity, and moderate, consistent activity.

RESULTS

After resting in daytime shelters, individuals travelled to multiple reefs throughout the night, traversing through depths of 2-112 m and temperatures of 10.0-23.8 °C. All sharks exhibited area restricted search patch use and arrived at their first patch approximately 3.4 ± 2.2 h (mean ± SD) after sunset. Sharks exhibited moderate, consistent activity in 54% of the patches used, episodic burst activity in 33%, and few (13%) were identified as resting at night. ODBA peaked while sharks were swimming through relatively deeper (~ 30 m), colder channels when traversing from one patch to the next. There was no consistent pattern between ODBA and temperature.

CONCLUSIONS

We provide one of the largest fine-scale, high-resolution paired data sets for an elasmobranch movement ecology study. Horn sharks exhibited ARS movement patterns for various activity patterns. Individuals likely travel to reefs known to have profitable and predictable patches, potentially tolerating less suitable environmental temperatures. We demonstrate how gathering high-resolution information on the movement decisions of a community resident enhances knowledge of community structure and overall ecosystem function.

Daily energy expenditure in white storks is lower after fledging than in the nest

Many juvenile birds turn into long-distance migrants within weeks of fledging. This transition involves upheavals in their energy management as major changes in growth and activity occur. Understanding such ontogenetic transitions in energy allocation has been difficult because collecting continuous data on energy costs in wild developing birds was previously largely impossible. Here, we continuously measured heart rate and fine-scale movements of 20 free-living juvenile white storks (Ciconia ciconia) using on-board bio-loggers to explore individual and environmental factors relating to daily mean heart rate. In addition, we explored which specific energy management strategy storks use during these crucial early life stages. We found that daily mean heart rate increased with overall movement activity, and increasing body temperature, but that it decreased with age. Further, we found that during the nestling period, when growth costs are high, activity costs are low, and post-fledging that activity costs are increased while maintenance costs are low, indicating a constraint on overall energy use in both phases. Our observations are consistent with the hypothesis that individuals invested more energy per unit time while still in the nest than after fledging despite the high costs of flight.

Diel patterns in swimming behavior of a vertically migrating deepwater shark, the bluntnose sixgill (Hexanchus griseus)

Diel vertical migration is a widespread behavioral phenomenon where organisms migrate through the water column and may modify behavior relative to changing environmental conditions based on physiological tolerances. Here, we combined a novel suite of biologging technologies to examine the thermal physiology (intramuscular temperature), fine-scale swimming behavior and activity (overall dynamic body acceleration as a proxy for energy expenditure) of bluntnose sixgill sharks (Hexanchus griseus) in response to environmental changes (depth, water temperature, dissolved oxygen) experienced during diel vertical migrations. In the subtropical waters off Hawai‘i, sixgill sharks undertook pronounced diel vertical migrations and spent considerable amounts of time in cold (5–7°C), low oxygen conditions (10–25% saturation) during their deeper daytime distribution. Further, sixgill sharks spent the majority of their deeper daytime distribution with intramuscular temperatures warmer than ambient water temperatures, thereby providing them with a significant thermal advantage over non-vertically migrating and smaller-sized prey. Sixgill sharks exhibited relatively high rates of activity during both shallow (night) and deep (day) phases and contrary to our predictions, did not reduce activity levels during their deeper daytime distribution while experiencing low temperature and dissolved oxygen levels. This demonstrates an ability to tolerate the low oxygen conditions occurring within the local oxygen minimum zone. The novel combination of biologging technologies used here enabled innovative in situ deep-sea natural experiments and provided significant insight into the behavioral and physiological ecology of an ecologically important deepwater species.

A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method

With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor conditions. The accuracy of two speed calculation methods was compared: one signal based and one machine learning model. Those two methods allowed the calculation of speed from accelerometric and gyroscopic data without any other external input. For this purpose, data were collected under various speeds on straight lines and curved paths. Two reference systems were used to measure the speed in order to have a reference speed value to compare each tested model and estimate their accuracy. Those models were compared according to three different criteria: the percentage of error above 0.6 m/s, the RMSE, and the Bland and Altman limit of agreement. The machine learning method outperformed its competitor by giving the lowest value for all three criteria. The main contribution of this work is that it is the first method that gives an accurate speed per stride for horses without being coupled with a global positioning system or a magnetometer. No similar study performed on horses exists to compare our work with, so the presented model is compared to existing models for human walking. Moreover, this tool can be extended to other equestrian sports, as well as bipedal locomotion as long as consistent data are provided to train the machine learning model. The machine learning model’s accurate results can be explained by the large database built to train the model and the innovative way of slicing stride data before using them as an input for the model.

Hormonal correlates of the annual cycle of activity and body temperature in the South-American tegu lizard (Salvator merianae)

Life history transitions and hormones are known to interact and influence many aspects of animal physiology and behavior. The South-American tegu lizard (Salvator merianae) exhibits a profound seasonal shift in metabolism and body temperature, characterized by high daily activity during warmer months, including reproductive endothermy in spring, and metabolic suppression during hibernation in winter. This makes S. merianae an interesting subject for studies of interrelationships between endocrinology and seasonal changes in physiology/behavior. We investigated how plasma concentrations of hormones involved in regulation of energy metabolism (thyroid hormones T₄ and T₃; corticosterone) and reproduction (testosterone in males and estrogen/progesterone in females) correlate with activity and body temperature (Tb) across the annual cycle of captive held S. merianae in semi-natural conditions. In our initial model, thyroid hormones and corticosterone showed a positive relationship with activity and Tb with independent of sex: T₃ positively correlated with activity and Tb, while T₄ and corticosterone correlated positively with changes in Tb only. This suggests that thyroid hormones and glucocorticoids may be involved in metabolic transitions of annual cycle events. When accounting for sex-steroid hormones, our sex separated models showed a positive relationship between testosterone and Tb in males and progesterone and activity in females. Coupling seasonal endocrine measures with activity and Tb may expand our understanding of the relationship between animal's physiology and its environment. Manipulative experiments are required in order to unveil the directionality of influences existing among abiotic factors and the hormonal signaling of annual cyclicity in physiology/behavior.

Time and energy costs of different foraging choices in an avian generalist species

BACKGROUND

Animals can obtain a higher foraging yield by optimizing energy expenditure or minimizing time costs. In this study, we assessed how individual variation in the relative use of marine and terrestrial foraging habitats relates to differences in the energy and time investments of an avian generalistic feeder (the Lesser Black-backed Gull, Larus fuscus), and how this changes during the course of the chick-rearing period.

METHODS

We analyzed 5 years of GPS tracking data collected at the colony of Zeebrugge (Belgium). Cost proxies for energy expenditure (overall dynamic body acceleration) and time costs (trip durations and time spent away from the colony), together with trip frequency, were analyzed against the relative use of the marine and terrestrial habitats.

RESULTS

The marine habitat was most often used by males and outside weekends, when fisheries are active. Marine trips implied higher energetic costs and lower time investments. As chicks became older, terrestrial trips became more prevalent, and trip frequency reached a peak towards 20 days after hatching of the first egg. Over a full chick rearing period, energy costs varied widely between individuals, but no trends were found across the marine foraging gradient. Conversely, a higher use of marine foraging implied lower overall amounts of time spent away from the colony.

CONCLUSIONS

Foraging habitat choice was related to overall time costs incurred by gulls, but not to energy costs. The effect of chick age on foraging habitat choice and effort may be driven by energy expenditure constraints on the amount of marine foraging that can be performed. If time is less constraining to them, Lesser Black-backed Gulls may meet the increasing chick demand for food by switching from high to low energy demanding foraging strategies.

A low-cost, open-source inertial movement GPS logger for eco-physiology applications

Open-source technology has been increasingly used for developing low-cost animal-borne bio-loggers; however, a gap remains for a bio-logger that records both inertial movement and GPS positions. We address this need with the Tapered Wings Logger (TWLogger), an archival bio-logger that records high-resolution (e.g. 50 Hz) tri-axial accelerometry and magnetometry, temperature and GPS. The TWLogger can be built for 90 USD, accepts user-defined sampling parameters, and with a 500 mA h battery weighs 25 g. We provide publicly available build instructions and custom analysis scripts. Bench tests recorded 50 Hz inertial movement and 2 min GPS for 31.8±2.2 h (mean±s.d., n=6) with GPS accuracy within 10.9±13.6 m. Field deployments on a medium-sized bird of prey in the wild achieved similar results (n=13). The customizable TWLogger has wide-ranging application across systems and thus offers a practical solution for eco-physiology applications.

Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China

Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300–800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30–50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (–3 m/s²) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.

Horsing Around—A Dataset Comprising Horse Movement

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

Behavioural compass: animal behaviour recognition using magnetometers

BACKGROUND

Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors.

METHODS

We address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition.

RESULTS

The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture.

CONCLUSIONS

Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens

Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals in situ. Here, we combine biologging with supervised machine learning (ML) to propose a pipeline for studying in situ behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on eight Chrysaora fuscescens in Monterey Bay, using the tether method for retrieval. By analyzing simultaneous video footage of the tagged jellyfish, we developed ML methods to: (1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and (2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and we conclude that it is essential to develop behavioral classifiers on in situ rather than laboratory data.

Summary: High-resolution motion sensors paired with supervised machine learning can be used to infer fine-scale in situ behavior of zooplankton over long durations.

"Closer-to-home" strategy benefits juvenile survival in a long-distance migratory bird

Human-induced changes in the climate and environment that occur at an unprecedented speed are challenging the existence of migratory species. Faced with these new challenges, species with diverse and flexible migratory behaviors may suffer less from population decline, as they may be better at responding to these changes by altering their migratory behavior. At the individual level, variations in migratory behavior may lead to differences in fitness and subsequently influence the population's demographic dynamics. Using lifetime GPS bio-logging data from 169 white storks (Ciconia ciconia), we explore whether the recently shortened migration distance of storks affects their survival during different stages of their juvenile life. We also explore how other variations in migratory decisions (i.e., time, destination), movement activity (measured using overall body dynamic acceleration), and early life conditions influence juvenile survival. We observed that their first autumn migration was the riskiest period for juvenile white storks. Individuals that migrated shorter distances and fledged earlier experienced lower mortality risks. In addition, higher movement activity and overwintering "closer-to-home" (with 84.21% of the tracked individuals stayed Europe or North Africa) were associated with higher survival. Our study shows how avian migrants can change life history decisions over only a few decades, and thus it helps us to understand and predict how migrants respond to the rapidly changing world.

Music Festival Makes Hedgehogs Move: How Individuals Cope Behaviorally in Response to Human-Induced Stressors

Simple Summary

Mega-events like concerts or festivals can still impact wildlife even when protective measures are taken. We remotely observed eight hedgehogs in a Berlin city park before and during a music festival using measuring devices attached to their bodies. While the actual festival only lasted two days (with about 70,000 visitors each day), setting the area up and removing the stages and stalls took 17 days in total. Construction work continued around the clock, causing an increase in light, noise and human presence throughout the night. In response, the hedgehogs showed clear changes in their behavior in comparison to the 19-day period just before the festival. We found, however, that different individuals responded differently to these changes in their environment. This individuality and behavioral flexibility could be one reason why hedgehogs are able to live in big cities.

Abstract

Understanding the impact of human activities on wildlife behavior and fitness can improve their sustainability. In a pilot study, we wanted to identify behavioral responses to anthropogenic stress in an urban species during a semi-experimental field study. We equipped eight urban hedgehogs (Erinaceus europaeus; four per sex) with bio-loggers to record their behavior before and during a mega music festival (2 × 19 days) in Treptower Park, Berlin. We used GPS (Global Positioning System) to monitor spatial behavior, VHF (Very High Frequency)-loggers to quantify daily nest utilization, and accelerometers to distinguish between different behaviors at a high resolution and to calculate daily disturbance (using Degrees of Functional Coupling). The hedgehogs showed clear behavioral differences between the pre-festival and festival phases. We found evidence supporting highly individual strategies, varying between spatial and temporal evasion of the disturbance. Averaging the responses of the individual animals or only examining one behavioral parameter masked these potentially different individual coping strategies. Using a meaningful combination of different minimal-invasive bio-logger types, we were able to show high inter-individual behavioral variance of urban hedgehogs in response to an anthropogenic disturbance, which might be a precondition to persist successfully in urban environments.

Water body type and group size affect the flight initiation distance of European waterbirds

Human encroachment on nature grows constantly, increasing human-wildlife interactions. Flight initiation distance (FID, the distance at which animals flee from an approaching threat) is often used to measure antipredator behaviour and establish buffer zones to reduce human impact on wildlife. In this study, we approached 10 waterbird species on larger lakes and narrower rivers using a motorboat. We investigated whether water body type, season (winter/spring), approach starting distance, species body mass, and group size influenced bird FID. Average bird FID was 145 ± 92 m and differed between species. In general, FID of all species was larger on lakes than rivers and increased with increasing group size and approach starting distance. When analysed separately for the two most common species, common goldeneyes (Bucephala clangula) and mallards (Anas platyrhynchos), FID increased with increasing starting distance on rivers, but not lakes, likely because birds on lakes have enough time to evaluate the approaching object and take flight at great distance. Additionally, birds might perform different activities on lakes versus rivers, leading to varying energetic trade-offs between the two habitat types, which may affect the decision when to take flight. Finally, mallards aggregated in larger groups on lakes, which affected FID, likely due to enhanced visibility (i.e., earlier detection of the approaching boat) and detection probability (via increased group size) on lakes. Our results emphasize the importance of accounting for habitat characteristics, such as water body type, when studying waterbird FID, because they can affect the visibility of stimuli, group size and potentially animal behaviour, factors which should be taken into account when planning buffer zones for waterbirds in conservation areas.

Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal-attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement-based power. Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings. Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement-related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non-movement-related factors, such as heat production, increases. Overall, DBA seems to be a substantive proxy for movement-based power but consideration of other movement-related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement-based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration.

Reproductive and Environmental Drivers of Time and Activity Budgets of Striped Skunks

The regulation of daily and circannual activity patterns is an important mechanism by which animals may balance energetic requirements associated with both abiotic and biotic variables. Using collar-mounted accelerometers, we assess the relative importance of reproductive stage and environmental conditions on the overall dynamic body acceleration (ODBA) of free-living striped skunks (Mephitis mephitis). We found that activity timing relative to photoperiod varied across seasonal stages for both sexes. Surprisingly, male skunks did not commence activity earlier than females during the mating interval. Moreover, while female skunks began activity before dusk and terminated activity after dawn during mid- through late summer (lactation period), the duration of activity bouts in females during this period was not different from other seasons. Both male and female skunks exhibited high variability and fragmentation in daily activity rhythms except during the lactation period, when females appear to switch to prolonged bouts of nocturnal activity. Overall, ODBA varied by season and sex, with changes in ODBA indicative of seasonal reproductive requirements such as conspecific competition for mates in males and lactation in females. Weather conditions had little effect on skunk activity levels except during the winter season, when snow cover and temperature negatively influenced daily ODBA. Taken together, the activity patterns of striped skunks appear to be primarily driven by seasonal investment in reproduction and secondarily by thermoregulatory constraints during the non-winter months. Our results highlight the importance of considering how environmental and reproductive drivers may interact to affect activity across both the daily and seasonal cycle.

Direct measurement of swimming and diving kinematics of giant Atlantic bluefin tuna (Thunnus thynnus)

Tunas possess a range of physiological and mechanical adaptations geared towards high-performance swimming that are of considerable interest to physiologists, ecologists and engineers. Advances in biologging have provided significant improvements in understanding tuna migrations and vertical movement patterns, yet our understanding of the locomotion and swimming mechanics of these fish under natural conditions is limited. We equipped Atlantic bluefin tuna (Thunnus thynnus) with motion-sensitive tags and video cameras to quantify the gaits and kinematics used by wild fish. Our data reveal significant variety in the locomotory kinematics of Atlantic bluefin tuna, ranging from continuous locomotion to two types of intermittent locomotion. The tuna sustained swimming speeds in excess of 1.5 m s-1 (0.6 body lengths s-1), while beating their tail at a frequency of approximately 1 Hz. While diving, some descents were entirely composed of passive glides, with slower descent rates featuring more gliding, while ascents were primarily composed of active swimming. The observed swimming behaviour of Atlantic bluefin tuna is consistent with theoretical models predicting such intermittent locomotion to result in mechanical and physiological advantages. Our results confirm that Atlantic bluefin tuna possess behavioural specializations to increase their locomotory performance, which together with their unique physiology improve their capacity to use pelagic and mesopelagic habitats.

The energetic cost of parasitism in a wild population

Parasites have profound fitness effects on their hosts, yet these are often sub-lethal, making them difficult to understand and quantify. A principal sub-lethal mechanism that reduces fitness is parasite-induced increase in energetic costs of specific behaviours, potentially resulting in changes to time and energy budgets. However, quantifying the influence of parasites on these costs has not been undertaken in free-living animals. We used accelerometers to estimate energy expenditure on flying, diving and resting, in relation to a natural gradient of endo-parasite loads in a wild population of European shags Phalacrocorax aristotelis. We found that flight costs were 10% higher in adult females with higher parasite loads and these individuals spent 44% less time flying than females with lower parasite loads. There was no evidence for an effect of parasite load on daily energy expenditure, suggesting the existence of an energy ceiling, with the increase in cost of flight compensated for by a reduction in flight duration. These behaviour specific costs of parasitism will have knock-on effects on reproductive success, if constraints on foraging behaviour detrimentally affect provisioning of young. The findings emphasize the importance of natural parasite loads in shaping the ecology and life-history of their hosts, which can have significant population level consequences.

Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water

Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free-ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free-ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free-ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer-based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer-based measures of energy expenditure strongly correlated (r 2 = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free-ranging animals.

Evidence of age-related improvement in the foraging efficiency of Adélie penguins

Age variation in reproductive performance is well-documented but the mechanisms underlying this variation remain unclear. Foraging efficiency is likely to be a key source of demographic variation as it determines the amount of energy that can be invested in fitness-related activities. Evidence of age-related changes in the foraging efficiency of adult seabirds is scarce and inconsistent. We investigated the effects of age on the foraging efficiency of breeding Adélie penguins, a relatively short-lived seabird species, in order to gain a broader perspective on the processes driving variation in ageing rates. We found support for a positive effect of age, either linear or levelling off at old ages, on both our proxies for daily catch rate and catch per unit effort. Across all age classes, males were more performant foragers than females. We found no strong evidence for differing ageing patterns between sexes or individual quality levels, and no evidence for senescence. We infer that continuous individual improvement could be responsible for a larger amount of the variation in foraging efficiency with age at our study site, compared with selective disappearance of underperforming phenotypes. The different results reported by other studies highlight the need to conduct longitudinal studies across a range of species in different environments.

Towards informed metrics for examining the role of human-induced animal responses in tag studies on wild animals

Two prime issues can detrimentally affect animals that have been equipped with tags: (i) the effect of the capture and restraint process; and (ii) the effect of the tag itself. This work examines some of the issues surrounding quantification of tag effects on wild animals for both restrained and free-living animals. A new method to quantify stress effects based on monitoring ventilation rates in relation to activity is suggested for restrained animals which may help improve the practice of handling animals. It is also suggested that various metrics, many derived from accelerometers, can be examined in tagged wild animals to examine the change in behaviors over time with a view to having a better understanding of welfare issues, assuring the quality of recorded data and informing best practice.

Why implantation of bio-loggers may improve our understanding of how animals cope within their natural environment

Bio-loggers are miniaturized autonomous devices that record quantitative data on the state of free-ranging animals (e.g. behavior, position and physiology) and their natural environment. This is especially relevant for species where direct visual observation is difficult or impossible. Today, ongoing technical development allows the monitoring of numerous parameters in an increasing range of species over extended periods. However, the external attachment of devices might affect various aspects of animal performance (energetics, thermoregulation, foraging as well as social and reproductive behavior), which ultimately affect fitness. External attachment might also increase entanglement risk and the conspicuousness of animals, leaving them more vulnerable to predation. By contrast, implantation of devices can mitigate many of these undesirable effects and might be preferable, especially for long-term studies, provided that the many challenges associated with surgical procedures can be mastered. Implantation may then allow us to gather data that would be impossible to obtain otherwise and thereby may provide new and ecologically relevant insights into the life of wild animals. Here, we: (i) discuss the pros and cons of attachment methods; (ii) highlight recent field studies that used implanted bio-loggers to address eco-physiological questions in a wide range of species; and (iii) discuss logger implantation in light of ethical considerations.

Swimming strategies and energetics of endothermic white sharks during foraging

Some fishes and sea turtles are distinct from ectotherms by having elevated core body temperatures and metabolic rates. Quantifying the energetics and activity of the regionally endothermic species will help us understand how a fundamental biophysical process (i.e. temperature-dependent metabolism) shapes animal ecology; however, such information is limited owing to difficulties in studying these large, highly active animals. White sharks, Carcharodon carcharias, are the largest fish with regional endothermy, and potentially among the most energy-demanding fishes. Here, we deployed multi-sensor loggers on eight white sharks aggregating near colonies of long-nosed fur seals, Arctocephalus forsteri, off the Neptune Islands, Australia. Simultaneous measurements of depth, swim speed (a proxy for swimming metabolic rate) and body acceleration (indicating when sharks exhibited energy-efficient gliding behaviour) revealed their fine-scale swimming behaviour and allowed us to estimate their energy expenditure. Sharks repeatedly dived (mean swimming depth, 29 m) and swam at the surface between deep dives (maximum depth, 108 m). Modal swim speeds (0.80–1.35 m s−1) were slower than the estimated speeds that minimize cost of transport (1.3–1.9 m s−1), a pattern analogous to a ‘sit-and-wait’ strategy for a perpetually swimming species. All but one shark employed unpowered gliding during descents, rendering deep (&gt;50 m) dives 29% less costly than surface swimming, which may incur additional wave drag. We suggest that these behavioural strategies may help sharks to maximize net energy gains by reducing swimming cost while increasing encounter rates with fast-swimming seals.

Kinematic signatures of prey capture from archival tags reveal sex differences in killer whale foraging activity

Studies of odontocete foraging ecology have been limited by the challenges of observing prey capture events and outcomes underwater. We sought to determine whether subsurface movement behavior recorded from archival tags could accurately identify foraging events by fish-eating killer whales. We used multisensor bio-logging tags attached by suction cups to Southern Resident killer whales (Orcinus orca) to: (1) identify a stereotyped movement signature that co-occurred with visually confirmed prey capture dives; (2) construct a prey capture dive detector and validate it against acoustically confirmed prey capture dives; and (3) demonstrate the utility of the detector by testing hypotheses about foraging ecology. Predation events were significantly predicted by peaks in the rate of change of acceleration (‘jerk peak’), roll angle and heading variance. Detection of prey capture dives by movement signatures enabled substantially more dives to be included in subsequent analyses compared with previous surface or acoustic detection methods. Males made significantly more prey capture dives than females and more dives to the depth of their preferred prey, Chinook salmon. Additionally, only half of the tag deployments on females (5 out of 10) included a prey capture dive, whereas all tag deployments on males exhibited at least one prey capture dive (12 out of 12). This dual approach of kinematic detection of prey capture coupled with hypothesis testing can be applied across odontocetes and other marine predators to investigate the impacts of social, environmental and anthropogenic factors on foraging ecology.

Static landscape features predict uplift locations for soaring birds across Europe

Soaring flight is a remarkable adaptation to reduce movement costs by taking advantage of atmospheric uplifts. The movement pattern of soaring birds is shaped by the spatial and temporal availability and intensity of uplifts, which result from an interaction of local weather conditions with the underlying landscape structure. We used soaring flight locations and vertical speeds of an obligate soaring species, the white stork (Ciconia ciconia), as proxies for uplift availability and intensity. We then tested if static landscape features such as topography and land cover, instead of the commonly used weather information, could predict and map the occurrence and intensity of uplifts across Europe. We found that storks encountering fewer uplifts along their routes, as determined by static landscape features, suffered higher energy expenditures, approximated by their overall body dynamic acceleration. This result validates the use of static features as uplift predictors and suggests the existence of a direct link between energy expenditure and static landscape structure, thus far largely unquantified for flying animals. Our uplift availability map represents a computationally efficient proxy of the distribution of movement costs for soaring birds across the world's landscapes. It thus provides a base to explore the effects of changes in the landscape structure on the energy expenditure of soaring birds, identify low-cost movement corridors and ultimately inform the planning of anthropogenic developments.

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

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

Quantifying the swimming gaits of veined squid (Loligo forbesi) using bio-logging tags

Squid are mobile, diverse, ecologically important marine organisms whose behavior and habitat use can have substantial impacts on ecosystems and fisheries. However, due in part to the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these free-swimming, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Here we apply a novel, high-resolution bio-logging tag (ITAG) to seven veined squid Loligo forbesi in a controlled experimental environment to quantify their short-term (24-hr) behavioral patterns. Tag accelerometer, magnetometer and pressure data were used to develop automated gait classification algorithms based on overall dynamic body acceleration, and a subset of the events were assessed and confirmed using concurrently collected video data. Finning, flapping, and jetting gaits were observed, with the low-acceleration finning gaits detected most often. The animals routinely used a finning gait to ascend (climb) and then glide during descent with fins extended in the tank's water column, a possible strategy to improve swimming efficiency for these negatively buoyant animals. Arms and mantle-first directional swimming were observed in approximately equal proportions, and the squid were slightly but significantly more active at night. These tag-based observations are novel for squid and suggest a more efficient mode of movement then suggested by some previous observations. The combination of sensing, classification, and estimation developed and applied here will enable the quantification of squid activity patterns in the wild to provide new biological information, such as in situ identification of behavioral states, temporal patterns, habitat requirements, energy expenditure, and interactions of squid through space-time in the wild.

Condition-dependent foraging strategies in a coastal seabird: evidence for the rich get richer hypothesis

The degree to which foraging individuals are able to appropriately modify their behaviors in response to dynamic environmental conditions and associated resource availability can have important fitness consequences. Despite an increasingly refined understanding of differences in foraging behavior between individuals, we still lack detailed characterizations of within-individual variation over space and time, and what factors may drive this variability. From 2014 to 2017, we used GPS transmitters and accelerometers to document foraging movements by breeding adult Brown Pelicans (Pelecanus occidentalis) in the northern Gulf of Mexico, where the prey landscape is patchy and dynamic at various scales. Assessments of traditional foraging metrics such as trip distance, linearity, or duration did not yield significant relationships between individuals. However, we did observe lower site fidelity and less variation in energy expenditure in birds of higher body condition, despite a population-level trend of increased fidelity as the breeding season progressed. These findings suggest that high-quality individuals are both more variable and more efficient in their foraging behaviors during a period of high energetic demand, consistent with a “rich get richer” scenario in which individuals in better condition are able to invest in more costly behaviors that provide higher returns. This work highlights the importance of considering behavioral variation at multiple scales, with particular reference to within-individual variation, to improve our understanding of foraging ecology in wild populations.

The role of parasitism in the energy management of a free-ranging bird

Parasites often prompt sub-lethal costs to their hosts by eliciting immune responses. These costs can be hard to quantify but are crucial to our understanding of the host's ecology. Energy is a fundamental currency to quantify these costs, as energetic trade-offs often exist between key fitness-related processes. Daily energy expenditure (DEE) comprises of resting metabolic rate (RMR) and energy available for activity, which are linked via the energy management strategy of an organism. Parasitism may play a role in the balance between self-maintenance and activity, as immune costs can be expressed in elevated RMR. Therefore, understanding energy use in the presence of parasitism enables mechanistic elucidation of potential parasite costs. Using a gradient of natural parasite load and proxies for RMR and DEE in a wild population of breeding European shags (Phalacrocorax aristotelis), we tested the effect of parasitism on maintenance costs as well as the relationship between proxies for RMR and DEE. We found a positive relationship between parasite load and our RMR proxy in females but not males, and no relationship between proxies for RMR and DEE. This provides evidence for increased maintenance costs in individuals with higher parasite loads and suggests the use of an allocation energy management strategy, whereby an increase to RMR creates restrictions on energy allocation to other activities. This is likely to have fitness consequences as energy allocated to immunity is traded off against reproduction. Our findings demonstrate that understanding energy management strategies alongside fitness drivers is central to understanding the mechanisms by which these drivers influence individual fitness.

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

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

Using accelerometers to develop time-energy budgets of wild fur seals from captive surrogates

Background

Accurate time-energy budgets summarise an animal's energy expenditure in a given environment, and are potentially a sensitive indicator of how an animal responds to changing resources. Deriving accurate time-energy budgets requires an estimate of time spent in different activities and of the energetic cost of that activity. Bio-loggers (e.g., accelerometers) may provide a solution for monitoring animals such as fur seals that make long-duration foraging trips. Using low resolution to record behaviour may aid in the transmission of data, negating the need to recover the device.

Methods

This study used controlled captive experiments and previous energetic research to derive time-energy budgets of juvenile Australian fur seals (Arctocephalus pusillus) equipped with tri-axial accelerometers. First, captive fur seals and sea lions were equipped with accelerometers recording at high (20 Hz) and low (1 Hz) resolutions, and their behaviour recorded. Using this data, machine learning models were trained to recognise four states-foraging, grooming, travelling and resting. Next, the energetic cost of each behaviour, as a function of location (land or water), season and digestive state (pre- or post-prandial) was estimated. Then, diving and movement data were collected from nine wild juvenile fur seals wearing accelerometers recording at high- and low- resolutions. Models developed from captive seals were applied to accelerometry data from wild juvenile Australian fur seals and, finally, their time-energy budgets were reconstructed.

Results

Behaviour classification models built with low resolution (1 Hz) data correctly classified captive seal behaviours with very high accuracy (up to 90%) and recorded without interruption. Therefore, time-energy budgets of wild fur seals were constructed with these data. The reconstructed time-energy budgets revealed that juvenile fur seals expended the same amount of energy as adults of similar species. No significant differences in daily energy expenditure (DEE) were found across sex or season (winter or summer), but fur seals rested more when their energy expenditure was expected to be higher. Juvenile fur seals used behavioural compensatory techniques to conserve energy during activities that were expected to have high energetic outputs (such as diving).

Discussion

As low resolution accelerometry (1 Hz) was able to classify behaviour with very high accuracy, future studies may be able to transmit more data at a lower rate, reducing the need for tag recovery. Reconstructed time-energy budgets demonstrated that juvenile fur seals appear to expend the same amount of energy as their adult counterparts. Through pairing estimates of energy expenditure with behaviour this study demonstrates the potential to understand how fur seals expend energy, and where and how behavioural compensations are made to retain constant energy expenditure over a short (dive) and long (season) period.

Correcting the Activity-Specific Component of Heart Rate Variability Using Dynamic Body Acceleration Under Free-Moving Conditions

Heart rate variability (HRV) analysis is a widely used technique to assess sympatho-vagal regulation in response to various internal or external stressors. However, HRV measurements under free-moving conditions are highly susceptible to subjects’ physical activity levels because physical activity alters energy metabolism, which inevitably modulates the cardiorespiratory system and thereby changes the sympatho-vagal balance, regardless of stressors. Thus, researchers must simultaneously quantify the effect of physical activity on HRV to reliably assess sympatho-vagal balance under free-moving conditions. In the present study, dynamic body acceleration (DBA), which was developed in the field of animal ecology as a quantitative proxy for activity-specific energy expenditure, was used as a factor to correct for physical activity when evaluating HRV in freely moving subjects. Body acceleration and heart inter-beat intervals were simultaneously measured in cattle and sheep, and the vectorial DBA and HRV parameters were evaluated at 5-min intervals. Next, the effects of DBA on the HRV parameters were statistically analyzed. The heart rate (HR) and most of the HRV parameters were affected by DBA in both animal species, and the inclusion of the effect of DBA in the HRV analysis greatly influenced the frequency domain and nonlinear HRV parameters. By removing the effect of physical activity quantified using DBA, we could fairly compare the stress levels of animals with different physical activity levels under different management conditions. Moreover, we analyzed and compared the HRV parameters before and after correcting for the mean HR, with and without inclusion of DBA. The results were somewhat unexpected, as the effect of DBA was a highly significant source of HRV also in parameters corrected for mean HR. In conclusion, the inclusion of DBA as a physical activity index is a simple and useful method for correcting the activity-specific component of HRV under free-moving conditions.

Estimating fish swimming metrics and metabolic rates with accelerometers: the influence of sampling frequency

Accelerometry is growing in popularity for remotely measuring fish swimming metrics, but appropriate sampling frequencies for accurately measuring these metrics are not well studied. This research examined the influence of sampling frequency (1-25 Hz) with tri-axial accelerometer biologgers on estimates of overall dynamic body acceleration (ODBA), tail-beat frequency, swimming speed and metabolic rate of bonefish Albula vulpes in a swim-tunnel respirometer and free-swimming in a wetland mesocosm. In the swim tunnel, sampling frequencies of ≥ 5 Hz were sufficient to establish strong relationships between ODBA, swimming speed and metabolic rate. However, in free-swimming bonefish, estimates of metabolic rate were more variable below 10 Hz. Sampling frequencies should be at least twice the maximum tail-beat frequency to estimate this metric effectively, which is generally higher than those required to estimate ODBA, swimming speed and metabolic rate. While optimal sampling frequency probably varies among species due to tail-beat frequency and swimming style, this study provides a reference point with a medium body-sized sub-carangiform teleost fish, enabling researchers to measure these metrics effectively and maximize study duration.

Improving Real-Life Estimates of Emotion Based on Heart Rate: A Perspective on Taking Metabolic Heart Rate Into Account

Extracting information about emotion from heart rate in real life is challenged by the concurrent effect of physical activity on heart rate caused by metabolic need. “Non-metabolic heart rate,” which refers to the heart rate that is caused by factors other than physical activity, may be a more sensitive and more universally applicable correlate of emotion than heart rate itself. The aim of the present article is to explore the evidence that non-metabolic heart rate, as it has been determined up until now, indeed reflects emotion. We focus on methods using accelerometry since these sensors are readily available in devices suitable for daily life usage. The evidence that non-metabolic heart rate as determined by existing methods reflect emotion is limited. Alternative possible routes are explored. We conclude that for real-life cases, estimating the type and intensity of activities based on accelerometry (and other information), and in turn use those to determine the non-metabolic heart rate for emotion is most promising.

Energetic costs of locomotion in bears: is plantigrade locomotion energetically economical?

Ursids are the largest mammals to retain a plantigrade posture. This primitive posture has been proposed to result in reduced locomotor speed and economy relative to digitigrade and unguligrade species, particularly at high speeds. Previous energetics research on polar bears (Ursus maritimus) found locomotor costs were more than double predictions for similarly sized quadrupedal mammals, which could be a result of their plantigrade posture or due to adaptations to their Arctic marine existence. To evaluate whether polar bears are representative of terrestrial ursids or distinctly uneconomical walkers, this study measured the mass-specific metabolism, overall dynamic body acceleration, and gait kinematics of polar bears and grizzly bears (Ursus arctos) trained to rest and walk on a treadmill. At routine walking speeds, we found polar bears and grizzly bears exhibited similar costs of locomotion and gait kinematics, but differing measures of overall dynamic body acceleration. Minimum cost of transport while walking in the two species (2.21 J kg^-1 m^-1) was comparable to predictions for similarly sized quadrupedal mammals, but these costs doubled (4.42 J kg^-1 m^-1) at speeds ≥5.4 km h^-1 Similar to humans, another large plantigrade mammal, bears appear to exhibit a greater economy while moving at slow speeds.