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

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast-paced behaviours (e.g. locomotion), whereas lower-frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

Incorporating otolith-isotope inferred field metabolic rate into conservation strategies

Fluctuating ocean conditions are rearranging whole networks of marine communities-from individual-level physiological thresholds to ecosystem function. Physiological studies support predictions from individual-level responses (biochemical, cellular, tissue, respiratory potential) based on laboratory experiments. The otolith-isotope method of recovering field metabolic rate has recently filled a gap for the bony fishes, linking otolith stable isotope composition to in situ oxygen consumption and experienced temperature estimates. Here, we review the otolith-isotope method focusing on the biochemical and physiological processes that yield estimates of field metabolic rate. We identify a multidisciplinary pathway in the application of this method, providing concrete research goals (field, modeling) aimed at linking individual-level physiological data to higher levels of biological organization. We hope that this review will provide researchers with a transdisciplinary 'roadmap', guiding the use of the otolith-isotope method to bridge the gap between individual-level physiology, observational field studies, and modeling efforts, while ensuring that in situ data is central in marine policy-making aimed at mitigating climatic and anthropogenic threats.

Determining energy expenditure in a large seabird using accelerometry

The trade off between energy gained and expended is the foundation of understanding how, why and when animals perform any activity. Based on the concept that animal movements have an energetic cost, accelerometry is increasingly being used to estimate energy expenditure. However, validation of accelerometry as an accurate proxy for field metabolic rate in free-ranging species is limited. In the present study, Australasian gannets (Morus serrator) from the Pope's Eye colony (38°16'42″S 144°41'48″E), south-eastern Australia, were equipped with GPS and tri-axial accelerometers and dosed with doubly labelled water (DLW) to measure energy expenditure during normal behaviour for 3-5 days. The correlation between daily energy expenditure from the DLW and vectorial dynamic body acceleration (VeDBA) was high for both a simple correlation and activity-specific approaches (R2=0.75 and 0.80, respectively). Varying degrees of success were observed for estimating at-sea metabolic rate from accelerometry when removing time on land using published energy expenditure constants (R2=0.02) or activity-specific approaches (R2=0.42). The predictive capacity of energy expenditure models for total and at-sea periods was improved by the addition of total distance travelled and proportion of the sampling period spent at sea during the night, respectively (R2=0.61-0.82). These results indicate that accelerometry can be used to estimate daily energy expenditure in free-ranging gannets and its accuracy may depend on the inclusion of movement parameters not detected by accelerometry.

Cryptic behavior and activity cycles of a small mammal keystone species revealed through accelerometry: a case study of Merriam's kangaroo rats (Dipodomys merriami)

BACKGROUND

Kangaroo rats are small mammals that are among the most abundant vertebrates in many terrestrial ecosystems in Western North America and are considered both keystone species and ecosystem engineers, providing numerous linkages between other species as both consumers and resources. However, there are challenges to studying the behavior and activity of these species due to the difficulty of observing large numbers of individuals that are small, secretive, and nocturnal. Our goal was to develop an integrated approach of miniaturized animal-borne accelerometry and radiotelemetry to classify the cryptic behavior and activity cycles of kangaroo rats and test hypotheses of how their behavior is influenced by light cycles, moonlight, and weather.

METHODS

We provide a proof-of-concept approach to effectively quantify behavioral patterns of small bodied (< 50 g), nocturnal, and terrestrial free-ranging mammals using large acceleration datasets by combining low-mass, miniaturized animal-borne accelerometers with radiotelemetry and advanced machine learning techniques. We developed a method of attachment and retrieval for deploying accelerometers, a non-disruptive method of gathering observational validation datasets for acceleration data on free-ranging nocturnal small mammals, and used these techniques on Merriam's kangaroo rats to analyze how behavioral patterns relate to abiotic factors.

RESULTS

We found that Merriam's kangaroo rats are only active during the nighttime phases of the diel cycle and are particularly active during later light phases of the night (i.e., late night, morning twilight, and dawn). We found no reduction in activity or foraging associated with moonlight, indicating that kangaroo rats are actually more lunarphilic than lunarphobic. We also found that kangaroo rats increased foraging effort on more humid nights, most likely as a mechanism to avoid cutaneous water loss.

CONCLUSIONS

Small mammals are often integral to ecosystem functionality, as many of these species are highly abundant ecosystem engineers driving linkages in energy flow and nutrient transfer across trophic levels. Our work represents the first continuous detailed quantitative description of fine-scale behavioral activity budgets in kangaroo rats, and lays out a general framework for how to use miniaturized biologging devices on small and nocturnal mammals to examine behavioral responses to environmental factors.

What are the Metabolic Rates of Marine Mammals and What Factors Impact this Value: A review

Over the past several decades, scientists have constructed bioenergetic models for marine mammals to assess potential population-level consequences following exposure to a disturbance, stressor, or environmental change, such as under the Population Consequences of Disturbance (pCOD) framework. The animal's metabolic rate (rate of energy expenditure) is a cornerstone for these models, yet the cryptic lifestyles of marine mammals, particularly cetaceans, have limited our ability to quantify basal (BMR) and field (FMR) metabolic rates using accepted 'gold standard' approaches (indirect calorimeter via oxygen consumption and doubly labeled water, respectively). Thus, alternate methods have been used to quantify marine mammal metabolic rates, such as extrapolating from known allometric relationships (e.g. Kleiber's mouse to elephant curve) and developing predictive relationships between energy expenditure and physiological or behavioral variables. To understand our current knowledge of marine mammal metabolic rates, we conducted a literature review (1900-2023) to quantify the magnitude and variation of metabolic rates across marine mammal groups. A compilation of data from studies using 'gold standard' methods revealed that BMR and FMR of different marine mammal species ranges from 0.2 to 3.6 and 1.1 to 6.1 x Kleiber, respectively. Mean BMR and FMR varied across taxa; for both measures odontocete levels were intermediate to higher values for otariids and lower values of phocids. Moreover, multiple intrinsic (e.g. age, sex, reproduction, molt, individual) and extrinsic (e.g. food availability, water temperature, season) factors, as well as individual behaviors (e.g. animal at water's surface or submerged, activity level, dive effort and at-sea behaviors) impact the magnitude of these rates. This review provides scientists and managers with a range of reliable metabolic rates for several marine mammal groups as well as an understanding of the factors that influence metabolism to improve the discernment for inputs into future bioenergetic models.

Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable

BACKGROUND

All behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s2) to standard units (watts) involves costly in-lab respirometry measurements, and there is a lack of viable substitutes for empirical calibration relationships when these are unavailable.

METHODS

We used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.

RESULTS

ACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean).

CONCLUSIONS

We found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

The fire of evolution: energy expenditure and ecology in primates and other endotherms

Total energy expenditure (TEE) represents the total energy allocated to growth, reproduction and body maintenance, as well as the energy expended on physical activity. Early experimental work in animal energetics focused on the costs of specific tasks (basal metabolic rate, locomotion, reproduction), while determination of TEE was limited to estimates from activity budgets or measurements of subjects confined to metabolic chambers. Advances in recent decades have enabled measures of TEE in free-living animals, challenging traditional additive approaches to understanding animal energy budgets. Variation in lifestyle and activity level can impact individuals' TEE on short time scales, but interspecific differences in TEE are largely shaped by evolution. Here, we review work on energy expenditure across the animal kingdom, with a particular focus on endotherms, and examine recent advances in primate energetics. Relative to other placental mammals, primates have low TEE, which may drive their slow pace of life and be an evolved response to the challenges presented by their ecologies and environments. TEE variation among hominoid primates appears to reflect adaptive shifts in energy throughput and allocation in response to ecological pressures. As the taxonomic breadth and depth of TEE data expand, we will be able to test additional hypotheses about how energy budgets are shaped by environmental pressures and explore the more proximal mechanisms that drive intra-specific variation in energy expenditure.

The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets

Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be challenging to analyse. We tested the performance of commonly used artificial intelligence tools on datasets of increasing volume and dimensionality. By collecting bio-logging data across several sampling seasons, datasets are inherently characterized by inter-individual variability. Such information should be considered when predicting behaviour. We integrated both unsupervised and supervised machine learning approaches to predict behaviours in two penguin species. The classified behaviours obtained from the unsupervised approach Expectation Maximisation were used to train the supervised approach Random Forest. We assessed agreement between the approaches, the performance of Random Forest on unknown data and the implications for the calculation of energy expenditure. Consideration of behavioural variability resulted in high agreement (> 80%) in behavioural classifications and minimal differences in energy expenditure estimates. However, some outliers with < 70% of agreement, highlighted how behaviours characterized by signal similarity are confused. We advise the broad bio-logging community, approaching these large datasets, to be cautious when upscaling predictions, as this might lead to less accurate estimates of behaviour and energy expenditure.

The Adrenal Cortisol Response to Increasing Ambient Temperature in Polar Bears (Ursus maritimus)

Our objective was to identify the upper ambient temperature threshold that triggers an increase in cortisol in response to increased thermoregulatory demands in polar bears. The results reported here include endocrine data collected over two years from 25 polar bears housed in 11 accredited zoological institutions across North America. The effects of ambient temperature, sex, age group (juvenile, adult, elderly), breeding season and humidity on fecal cortisol metabolite (FCM) concentrations (N = 8439 samples) were evaluated using linear mixed models. Ambient temperatures were placed into five different categories: <5 °C, 6−10 °C, 11−15 °C, 16−20 °C, and >20 °C. Ambient temperature and humidity had a significant (p < 0.05) effect on FCM concentrations with significant (p < 0.05) interactions of sex, age and breeding season. Once biotic factors were accounted for, there was a significant (p < 0.05) increase in FCM concentrations associated with ambient temperatures above 20 °C in adult polar bears. The implications of these findings for the management of both zoo and wild polar bears are discussed.

Ecological inference using data from accelerometers needs careful protocols

Accelerometers in animal‐attached tags are powerful tools in behavioural ecology, they can be used to determine behaviour and provide proxies for movement‐based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, using data repositories to draw ecological inference requires a good understanding of the error introduced according to sensor type and position on the study animal and protocols for error assessment and minimisation.

Using laboratory trials, we examine the absolute accuracy of tri‐axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), a proxy for energy expenditure, in human participants. We then examine how tag type and placement affect the acceleration signal in birds, using pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, and back‐ and tail‐mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we present a case study where two generations of tag were deployed using different attachment procedures on red‐tailed tropicbirds Phaethon rubricauda foraging in different seasons.

Bench tests showed that individual acceleration axes required a two‐level correction to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at a range of speeds. Device position was associated with greater variation in DBA, with upper and lower back‐mounted tags varying by 9% in pigeons, and tail‐ and back‐mounted tags varying by 13% in kittiwakes. The tropicbird study highlighted the difficulties of attributing changes in signal amplitude to a single factor when confounding influences tend to covary, as DBA varied by 25% between seasons.

Accelerometer accuracy, tag placement and attachment critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that can be executed under field conditions. This should be used prior to deployments and archived with resulting data. We also suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

Field metabolic rates of giant pandas reveal energetic adaptations

Knowledge of energy expenditure informs conservation managers for long term plans for endangered species health and habitat suitability. We measured field metabolic rate (FMR) of free-roaming giant pandas in large enclosures in a nature reserve using the doubly labeled water method. Giant pandas in zoo like enclosures had a similar FMR (14,182 kJ/day) to giant pandas in larger field enclosures (13,280 kJ/day). In winter, giant pandas raised their metabolic rates when living at − 2.4 °C (36,108 kJ/day) indicating that they were below their thermal neutral zone. The lower critical temperature for thermoregulation was about 8.0 °C and the upper critical temperature was about 28 °C. Giant panda FMRs were somewhat lower than active metabolic rates of sloth bears, lower than FMRs of grizzly bears and polar bears and 69 and 81% of predicted values based on a regression of FMR versus body mass of mammals. That is probably due to their lower levels of activity since other bears actively forage for food over a larger home range and pandas often sit in a patch of bamboo and eat bamboo for hours at a time. The low metabolic rates of giant pandas in summer, their inability to acquire fat stores to hibernate in winter, and their ability to raise their metabolic rate to thermoregulate in winter are energetic adaptations related to eating a diet composed almost exclusively of bamboo. Differences in FMR of giant pandas between our study and previous studies (one similar and one lower) appear to be due to differences in activity of the giant pandas in those studies.

Total energy expenditure of bottlenose dolphins (Tursiops truncatus) of different ages

Marine mammals are thought to have an energetically expensive lifestyle because endothermy is costly in marine environments. However, measurements of total energy expenditure (TEE; kcal day-1) are available only for a limited number of marine mammals, because large body size and inaccessible habitats make TEE measurements expensive and difficult to obtain for many taxa. We measured TEE in 10 adult common bottlenose dolphins (Tursiops truncatus) living in natural seawater lagoons at two facilities (Dolphin Research Center and Dolphin Quest) using the doubly labeled water method. We assessed the relative effects of body mass, age and physical activity on TEE. We also examined whether TEE of bottlenose dolphins, and more generally of marine mammals, differs from that expected for their body mass compared with other eutherian mammals, using phylogenetic least squares (PGLS) regressions. There were no differences in body mass or TEE (unadjusted TEE and TEE adjusted for fat-free mass) between dolphins from the two facilities. Our results show that adjusted TEE decreased and fat mass increased with age. Different measures of activity were not related to age, body fat or adjusted TEE. Both PGLS and the non-phylogenetic linear regression indicate that marine mammals have an elevated TEE compared with that of terrestrial mammals. However, bottlenose dolphins expended 17.1% less energy than other marine mammals of similar body mass. The two oldest dolphins (>40 years) showed a lower TEE, similar to the decline in TEE seen in older humans. To our knowledge, this is the first study to show an age-related metabolic decline in a large non-human mammal.

Validating accelerometry-derived proxies of energy expenditure using the doubly labelled water method in the smallest penguin species

Understanding energy use is central to understanding an animal's physiological and behavioural ecology. However, directly measuring energy expenditure in free-ranging animals is inherently difficult. The doubly labelled water (DLW) method is widely used to investigate energy expenditure in a range of taxa. Although reliable, DLW data collection and analysis is both financially costly and time consuming. Dynamic body acceleration (e.g. VeDBA) calculated from animal-borne accelerometers has been used to determine behavioural patterns, and is increasingly being used as a proxy for energy expenditure. Still its performance as a proxy for energy expenditure in free-ranging animals is not well established and requires validation against established methods. In the present study, the relationship between VeDBA and the at-sea metabolic rate calculated from DLW was investigated in little penguins (Eudyptula minor) using three approaches. Both in a simple correlation and activity-specific approaches were shown to be good predictors of at-sea metabolic rate. The third approach using activity-specific energy expenditure values obtained from literature did not accurately calculate the energy expended by individuals. However, all three approaches were significantly strengthened by the addition of mean horizontal travel speed. These results provide validation for the use of accelerometry as a proxy for energy expenditure and show how energy expenditure may be influenced by both individual behaviour and environmental conditions.

Accelerometer informed time-energy budgets reveal the importance of temperature to the activity of a wild, arid zone canid

BACKGROUND

Globally, arid regions are expanding and becoming hotter and drier with climate change. For medium and large bodied endotherms in the arid zone, the necessity to dissipate heat drives a range of adaptations, from behaviour to anatomy and physiology. Understanding how apex predators negotiate these landscapes and how they balance their energy is important as it may have broad impacts on ecosystem function.

METHODS

We used tri-axial accelerometry (ACC) and GPS data collected from free-ranging dingoes in central Australia to investigate their activity-specific energetics, and activity patterns through time and space. We classified dingo activity into stationary, walking, and running behaviours, and estimated daily energy expenditure via activity-specific time-energy budgets developed using energy expenditure data derived from the literature. We tested whether dingoes behaviourally thermoregulate by modelling ODBA as a function of ambient temperature during the day and night. We used traditional distance measurements (GPS) as well as fine-scale activity (ODBA) data to assess their daily movement patterns.

RESULTS

We retrieved ACC and GPS data from seven dingoes. Their mass-specific daily energy expenditure was significantly lower in summer (288 kJ kg- 1 day- 1) than winter (495 kJ kg- 1 day- 1; p = 0.03). Overall, dingoes were much less active during summer where 91% of their day was spent stationary in contrast to just 46% during winter. There was a sharp decrease in ODBA with increasing ambient temperature during the day (R2 = 0.59), whereas ODBA increased with increasing Ta at night (R2 = 0.39). Distance and ODBA were positively correlated (R = 0.65) and produced similar crepuscular patterns of activity.

CONCLUSION

Our results indicate that ambient temperature may drive the behaviour of dingoes. Seasonal differences of daily energy expenditure in free-ranging eutherian mammals have been found in several species, though this was the first time it has been observed in a wild canid. We conclude that the negative relationship between dingo activity (ODBA) and ambient temperature during the day implies that high heat gain from solar radiation may be a factor limiting diurnal dingo activity in an arid environment.

Acceleration predicts energy expenditure in a fat, flightless, diving bird

Energy drives behaviour and life history decisions, yet it can be hard to measure at fine scales in free-moving animals. Accelerometry has proven a powerful tool to estimate energy expenditure, but requires calibration in the wild. This can be difficult in some environments, or for particular behaviours, and validations have produced equivocal results in some species, particularly air-breathing divers. It is, therefore, important to calibrate accelerometry across different behaviours to understand the most parsimonious way to estimate energy expenditure in free-living conditions. Here, we combine data from miniaturised acceleration loggers on 58 free-living Adélie penguins with doubly labelled water (DLW) measurements of their energy expenditure over several days. Across different behaviours, both in water and on land, dynamic body acceleration was a good predictor of independently measured DLW-derived energy expenditure (R2 = 0.72). The most parsimonious model suggested different calibration coefficients are required to predict behaviours on land versus foraging behaviour in water (R2 = 0.75). Our results show that accelerometry can be used to reliably estimate energy expenditure in penguins, and we provide calibration equations for estimating metabolic rate across several behaviours in the wild.

Factors affecting energy expenditure in a declining fur seal population

Quantifying metabolic rates and the factors that influence them is key to wildlife conservation efforts because anthropogenic activities and habitat alteration can disrupt energy balance, which is critical for reproduction and survival. We investigated the effect of diving behaviour, diet and season on field metabolic rates (FMR) and foraging success of lactating northern fur seals (Callorhinus ursinus) from the Pribilof Islands during a period of population decline. Variation in at-sea FMR was in part explained by season and trip duration, with values that ranged from 5.18 to 9.68 W kg^-1 (n = 48). Fur seals experienced a 7.2% increase in at-sea FMR from summer to fall and a 1.9% decrease in at-sea FMR for each additional day spent at sea. There was no effect of foraging effort, dive depth or diet on at-sea FMR. Mass gains increased with trip duration and were greater in the fall compared with summer, but were unrelated to at-sea FMR, diving behaviour and diet. Seasonal increases in at-sea FMR may have been due to costs associated with the annual molt but did not appear to adversely impact the ability of females to gain mass on foraging trips. The overall high metabolic rates in conjunction with the lack of any diet-related effects on at-sea FMR suggests that northern fur seals may have reached a metabolic ceiling early in the population decline. This provides indirect evidence that food limitation may be contributing to the low pup growth rates observed in the Pribilof Islands, as a high metabolic overhead likely results in less available energy for lactation. The limited ability of female fur seals to cope with changes in prey availability through physiological mechanisms is particularly concerning given the recent and unprecedented environmental changes in the Bering Sea that are predicted to have ecosystem-level impacts.

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.