Can ethograms be automatically generated using body acceleration data from free-ranging birds?

Heart rate reduction during voluntary deep diving in free-ranging loggerhead sea turtles

Air-breathing vertebrates exhibit cardiovascular responses to diving including heart rate reduction (diving bradycardia). Field studies on aquatic mammals and birds have shown that the intensity of bradycardia can vary depending on diving behaviour, such as the depth of dives and dive duration. However, in aquatic reptiles, the variation in heart rate during deep dives under natural conditions has not been fully investigated. In this study, we released five loggerhead sea turtles (Caretta caretta) outfitted with recorders into the sea and recorded their electrocardiogram, depth, water temperature and longitudinal acceleration. After 3 days, the recorders automatically detached from the turtles. The heart rate signals were detected from the electrodes placed on the surface of the plastron. The mean (±s.d.) heart rate of 12.8±4.1 beats min-1 during dives was significantly lower than that of 20.9±4.1 beats min-1 during surface periods. Heart rate during dives varied with dive depth, although it remained lower than that at the surface. When the turtle dived deeper than 140 m, despite the relatively high flipper stroke rate (approximately 19 strokes min-1), the heart rate dropped rapidly to approximately 2 beats min-1 temporarily. The minimum instantaneous heart rate during dives was lower at deeper dive depths. Our results indicate that loggerhead sea turtles show variations in the intensity of diving bradycardia depending on their diving behaviour, similar to that shown by marine mammals and birds.

Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector

Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g. video cameras). In this study, we propose an artificial intelligence (AI)-enabled bio-logger that automatically detects outlier readings from always-on low-cost sensors, e.g. accelerometers, indicative of rare behaviors in target animals, without supervision by researchers, subsequently activating high-cost sensors to record only these behaviors. We implemented an on-board outlier detector via knowledge distillation by building a lightweight outlier classifier supervised by a high-cost outlier behavior detector trained in an unsupervised manner. The efficacy of AI bio-loggers has been demonstrated on seabirds, where videos and sensor data captured by the bio-loggers have enabled the identification of some rare behaviors, facilitating analyses of their frequency, and potential factors underlying these behaviors. This approach offers a means of documenting previously overlooked rare behaviors, augmenting our understanding of animal behavior.

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.

Direct measurement of cruising and burst swimming speeds of the shortfin mako shark (Isurus oxyrinchus) with estimates of field metabolic rate

The shortfin mako shark is a large-bodied pursuit predator thought to be capable of the highest swimming speeds of any elasmobranch and potentially one of the highest energetic demands of any marine fish. Nonetheless, few direct speed measurements have been reported for this species. Here, animal-borne bio-loggers attached to two mako sharks were used to provide direct measurements of swimming speeds, kinematics and thermal physiology. Mean sustained (cruising) speed was 0.90 m s-1 (±0.07 s.d.) with a mean tail-beat frequency (TBF) of 0.51 Hz (±0.16 s.d.). The maximum burst speed recorded was 5.02 m s-1 (TBFmax  = 3.65 Hz) from a 2 m long female. Burst swimming was sustained for 14 s (mean speed = 2.38 m s-1 ), leading to a 0.24°C increase in white muscle temperature in the 12.5 min after the burst. Routine field metabolic rate was estimated at 185.2 mg O2 kg-1  h-1 (at 18°C ambient temperature). Gliding behaviour (zero TBF) was more frequently observed after periods of high activity, especially after capture when internal (white muscle) temperature approached 21°C (ambient temperature: 18.3°C), indicating gliding probably functions as an energy recovery mechanism and limits further metabolic heat production. The results show shortfin mako sharks generally cruise at speeds similar to other endothermic fish - but faster than ectothermic sharks - with the maximum recorded burst speed being among the highest so far directly measured among sharks, tunas and billfishes. This newly recorded high-oxygen-demand performance of mako sharks suggests it may be particularly vulnerable to habitat loss due to climate-driven ocean deoxygenation.

Back to the wild: movements of a juvenile tiger shark released from a public aquarium

Sharks are an important attraction for aquaria; however, larger species can rarely be kept indefinitely. To date, there has been little work tracking shark movements post-release to the wild. The authors used high-resolution biologgers to monitor a sub-adult tiger shark's pre- and post-release fine-scale movements following 2 years of captivity in an aquarium. They also compared its movement with that of a wild shark tagged nearby. Despite the differences in movement between the two sharks, with vertical oscillations notably absent and greater levels of turning seen from the released shark, the captive shark survived the release. These biologgers improve insight into post-release movements of captive sharks.

A novel method for automatically analysing the activity of fast-moving animals: a case study of Callimico goeldii monkeys housed in a zoological garden

Behavioural indices are recognised as important criteria for assessing animal welfare. One of the basic animal behaviours included in ethograms is their activity. The assessment of fast-moving animals, performed by humans using the visual observation method, is difficult and not very objective. Therefore, the aim of the research was to develop a method of automated analysis of animal activity, particularly useful in the observation of quick and lively individuals, and to prove its suitability for assessing the behaviour of fast-moving animals. A method of automatically assessing animal activity was developed using digital image analysis, with the Python programming language and the OpenCV library being the foundational tools. The research model was Callimico goeldii monkeys housed in a zoological garden. This method has been proved to correlate well (Rs = 0.76) with the visual method of animal behaviour analysis. The developed automatic evaluation of animal behaviour is many times faster than visual analysis, and it enables precise assessment of the daily activity of fast-moving groups of animals. The use of this system makes it possible to obtain an activity index with sub-second resolution, which allows it to be used in online mode as a detector of abnormal animal activity, e.g. early detection of illnesses or sudden events that are manifested by increased or decreased activity in relation to the standard activity pattern.

"Breath holding" as a thermoregulation strategy in the deep-diving scalloped hammerhead shark

Fish moving between different thermal environments experience heat exchange via conduction through the body wall and convection from blood flow across the gills. We report a strategy of preventing convective heat loss at the gills during excursions into deep, cold water by the tropical scalloped hammerhead shark (Sphryna lewini). Adult scalloped hammerhead sharks dive rapidly and repeatedly from warm (~26°C) surface waters to depths exceeding 800 meters with temperatures as low as 5°C. Biologgers attached to adult sharks show that warm muscle temperatures were maintained throughout the deepest portion of each dive. Substantive cooling only occurred during the latter stages of the ascent phase and, once initiated, was rapid. Heat transfer coefficient modeling indicated that convective heat transfer was suspended, probably by suppressing gill function during deep dives. This previously unobserved strategy has broad similarities to marine mammal "breath hold" diving.

Supervised versus unsupervised approaches to classification of accelerometry data

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation-maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: -0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

First insights into the shortfin mako shark (Isurus oxyrinchus) fine-scale swimming behaviour

As regional endotherms, lamnid sharks can sustain high cruising speeds and perform frequent speed bursts. However, since endothermy comes with high energetic costs, lamnids may adopt different swimming strategies to manage their energy budget. Understanding such strategies is essential to provide behavioural and physiological context to their broader movement ecology. The endangered shortfin mako (Isurus oxyrinchus) possibly has the highest energy requirements among lamnids, but our understanding of its swimming behaviour is still limited. We equipped three shortfin mako sharks with high-resolution multi-sensor tags to measure their swimming kinematics in the wild. While swimming horizontally, individuals favoured tail-beat frequencies around 0.6 Hz at speeds comparable to those of ectothermic sharks (ca 0.5 m s^-1). All individuals displayed yo-yo-like diving patterns where, for a given tail-beat frequency, speeds were higher during descents, as expected for a negatively buoyant fish. Contrary to what was expected, gliding was almost absent (less than 1.31%). Speed bursts reaching up to 3.6 m s^-1 were observed during the day but ceased shortly after dusk, implying a diel change in swimming behaviour. As large-scale research efforts are hindered by this species' increasing rarity, opportunistic high-resolution datasets, like the present, are fundamental to improve our understanding of shortfin mako's behaviour and ecology.

Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning

Abstract

Accelerometers capture rapid changes in animal motion, and the analysis of large quantities of such data using machine learning algorithms enables the inference of broad animal behaviour categories such as foraging, flying, and resting over long periods of time. We deployed GPS-GSM/GPRS trackers with tri-axial acceleration sensors on common woodpigeons (Columba palumbus) from Hesse, Germany (forest and urban birds) and from Lisbon, Portugal (urban park). We used three machine learning algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to classify the main behaviours of the birds, namely foraging, flying, and resting and calculated time budgets over the breeding and winter season. Woodpigeon time budgets varied between seasons, with more foraging time during the breeding season than in winter. Also, woodpigeons from different sites showed differences in the time invested in foraging. The proportion of time woodpigeons spent foraging was lowest in the forest habitat from Hesse, higher in the urban habitat of Hesse, and highest in the urban park in Lisbon. The time budgets we recorded contrast to previous findings in woodpigeons and reaffirm the importance of considering different populations to fully understand the behaviour and adaptation of a particular species to a particular environment. Furthermore, the differences in the time budgets of Woodpigeons from this study and previous ones might be related to environmental change and merit further attention and the future investigation of energy budgets.

Significance statement

In this study we took advantage of accelerometer technology and machine learning methods to investigate year-round behavioural time budgets of wild common woodpigeons (Columba palumbus). Our analysis focuses on identifying coarse-scale behaviours (foraging, flying, resting) using various machine learning algorithms. Woodpigeon time budgets varied between seasons and among sites. Particularly interesting is the result showing that urban woodpigeons spend more time foraging than forest conspecifics. Our study opens an opportunity to further investigate and understand how a successful bird species such as the woodpigeon copes with increasing environmental change and urbanisation. The increase in the proportion of time devoted to foraging might be one of the behavioural mechanisms involved but opens questions about the costs associated to such increase in terms of other important behaviours.

Mercury Contamination Challenges the Behavioral Response of a Keystone Species to Arctic Climate Change

Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Arctic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioral responses to changes in foraging landscapes caused by climate change, generating interactive effects on behavior and population resilience. We investigated this hypothesis in little auks (Alle alle), a keystone Arctic seabird. We compiled behavioral data for 44 birds across 5 years using accelerometers while also quantifying blood Hg and environmental conditions. Warm sea surface temperature (SST) and low sea ice coverage reshaped time activity budgets (TABs) and diving patterns, causing decreased resting, increased flight, and longer dives. Mercury contamination was not associated with TABs. However, highly contaminated birds lengthened interdive breaks when making long dives, suggesting Hg-induced physiological limitations. As dive durations increased with warm SST, subtle toxicological effects threaten to increasingly constrain diving and foraging efficiency as climate change progresses, with ecosystem-wide repercussions.

Hunting behavior of a solitary sailfish Istiophorus platypterus and estimated energy gain after prey capture

Foraging behavior and interaction with prey is an integral component of the ecological niche of predators but is inherently difficult to observe for highly mobile animals in the marine environment. Billfishes have been described as energy speculators, expending a large amount of energy foraging, expecting to offset high costs with periodic high energetic gain. Surface-based group feeding of sailfish, Istiophorus platypterus, is commonly observed, yet sailfish are believed to be largely solitary roaming predators with high metabolic requirements, suggesting that individual foraging also represents a major component of predator-prey interactions. Here, we use biologging data and video to examine daily activity levels and foraging behavior, estimate metabolic costs, and document a solitary predation event for a 40 kg sailfish. We estimate a median active metabolic rate of 218.9 ± 70.5 mgO₂ kg^-1 h^-1 which increased to 518.8 ± 586.3 mgO₂ kg^-1 h^-1 during prey pursuit. Assuming a successful predation, we estimate a daily net energy gain of 2.4 MJ (5.1 MJ acquired, 2.7 MJ expended), supporting the energy speculator model. While group hunting may be a common activity used by sailfish to acquire energy, our calculations indicate that opportunistic individual foraging events offer a net energy return that contributes to the fitness of these highly mobile predators.

Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data

BACKGROUND

Studies of animal behaviour, ecology and physiology are continuously benefitting from progressing biologging techniques, including the collection of accelerometer data to infer animal behaviours and energy expenditure. In one of the most recent technological advances in this space, on-board processing of raw accelerometer data into animal behaviours proves highly energy-, weight- and cost-efficient allowing for continuous behavioural data collection in addition to regular positional data in a wide range of animal tracking studies.

METHODS

We implemented this latest development in collecting continuous behaviour records from 6 Pacific Black Ducks Anas superciliosa to evaluate some of this novel technique's potential advantages over tracking studies lacking behavioural data or recording accelerometer data intermittently only. We (i) compared the discrepancy of time-activity budgets between continuous records and behaviours sampled with different intervals, (ii) compared total daily distance flown using hourly GPS fixes with and without additional behavioural data and (iii) explored how behaviour records can provide additional insights for animal home range studies.

RESULTS

Using a total of 690 days of behaviour records across six individual ducks distinguishing eight different behaviours, we illustrated the improvement that is obtained in time-activity budget accuracy if continuous rather than interval-sampled accelerometer data is used. Notably, for rare behaviours such as flying and running, error ratios > 1 were common when sampling intervals exceeded 10 min. Using 72 days of hourly GPS fixes in combination with continuous behaviour records over the same period in one individual duck, we showed behaviour-based daily distance estimation is significantly higher (up to 540%) than the distance calculated from hourly sampled GPS fixes. Also, with the same 72 days of data for one individual duck, we showed how this individual used specific sites within its entire home range to satisfy specific needs (e.g. roosting and foraging).

CONCLUSION

We showed that by using trackers allowing for continuous recording of animal behaviour, substantial improvements in the estimation of time-activity budgets and daily traveling distances can be made. With integrating behaviour into home-range estimation we also highlight that this novel tracking technique may not only improve estimations but also open new avenues in animal behaviour research, importantly improving our knowledge of an animal's state while it is roaming the landscape.

Capture heats up sharks

Catch-and-release fishing is an important component of ecotourism industries and scientific research worldwide, but its total impact on animal physiology, health and survival is understudied for many species of fishes, particularly sharks. We combined biologging and blood chemistry to explore how this fisheries interaction influenced the physiology of two widely distributed, highly migratory shark species: the blue shark (Prionace glauca) and the tiger shark (Galeocerdo cuvier). Nineteen sharks were caught by drum line or rod-and-reel angling; subcutaneous body temperature measurements were taken immediately upon capture, with six individuals also providing subsequent subcutaneous body temperature measurements via biologging as they swam freely for several hours post-release. We found that short-term capture caused shark body temperature to increase significantly and rapidly, with increases of 0.6°C-2.7°C for blue sharks (mean, 1.2 ± 0.6°C) and 0.5°C-0.9°C for tiger sharks (mean, 0.7 ± 0.2°C) and with capture-induced heating rates of blue sharks averaging 0.3°C min^-1 but as high as 0.8°C min^-1. Blue shark body temperature was even higher deeper into the white muscle. These heating rates were three to eight times faster than maximum rates encountered by our biologging sharks swimming through thermally stratified waters and faster than most acute heating experiments conducted with ectotherms in laboratory experiments. Biologging data showed that body temperatures underwent gradual decline after release, returning to match water temperatures 10-40 mins post-release. Blood biochemistry showed variable lactate/glucose levels following capture; however, these concentrations were not correlated with the magnitude of body temperature increase, nor with body size or hooking time. These perturbations of the natural state could have immediate and longer-term effects on the welfare and ecology of sharks caught in catch-and-release fisheries and we encourage further study of the broader implications of this reported phenomenon.

Short-term behavioural responses of Atlantic bluefin tuna to catch-and-release fishing

Catch-and-release (C&R) angling is often touted as a sustainable form of ecotourism, yet the fine-scale behaviour and physiological responses of released fish is often unknown, especially for hard-to-study large pelagic species like Atlantic bluefin tuna (ABFT; Thunnus thunnus). Multi-channel sensors were deployed and recovered from 10 ABFTs in a simulated recreational C&R event off the west coast of Ireland. Data were recorded from 6 to 25 hours, with one ABFT (tuna X) potentially suffering mortality minutes after release. Almost all ABFTs (n = 9, including tuna X) immediately and rapidly (vertical speeds of ~2.0 m s^-1) made powered descents and used 50-60% of the available water column within 20 seconds, before commencing near-horizontal swimming ~60 seconds post-release. Dominant tailbeat frequency was ~50% higher in the initial hours post-release and appeared to stabilize at 0.8-1.0 Hz some 5-10 hours post-release. Results also suggest different short-term behavioural responses to noteworthy variations in capture and handling procedures (injury and reduced air exposure events). Our results highlight both the immediate and longer-term effects of C&R on ABFTs and that small variations in C&R protocols can influence physiological and behavioural responses of species like the commercially valuable and historically over-exploited ABFT.

Rapid endothermal development of juvenile pacific bluefin tuna

An important trait of Pacific bluefin tuna (PBT) is their ability to maintain their body temperature above the ambient temperature, which allows them to occupy a wider ecological niche. However, the size at which this ability in nature develops is unclear. Therefore, this study aimed to clarify this point by monitoring the body temperature and the surrounding ambient temperature as the fish grew. PBT with fork lengths (FLs) ranging from 19.5 to 28.0 cm were implanted with archival electronic tags and released into the ocean. Data from 41 fish were obtained (recorded body and water temperatures, light level, and swimming depth (pressure) at 30-s intervals) and analyzed to elucidate the development of the ability of PBT to maintain a high body temperature. Body temperature of a PBT (< FL of ca. 40 cm) decreased in response to a vertical movement down to cooler depths, but higher body temperatures were maintained as the fish grew. The body temperature was then continuously maintained above ambient temperatures and fluctuated independently when fish attained more than 40 cm FL. Estimation of the whole-body heat-transfer coefficient and heat-production rate indicated that the latter decreased slowly with growth, while the former decreased by one order of magnitude when tuna reached 52 cm FL. Additionally, in the daytime, the whole-body heat-transfer coefficient was significantly higher than that at nighttime. Unlike other fishes including other Thunnus species, inhabiting tropical/subtropical waters, PBT rapidly acquire higher thermo-conservation ability when young, allowing capture of high-quality prey abundant in temperate waters to support high growth rates during early life.

Whiskers as hydrodynamic prey sensors in foraging seals

Unlike humans, most mammals have mobile facial whiskers, yet their natural movement and function are unknown due to observational difficulties, even in well-studied terrestrial whisker specialists (rodents). We report a remarkable case of whiskers contributing to mammal foraging in an extreme underwater environment: the deep, dark ocean. Our animal-borne video cameras revealed that elephant seals captured moving prey by sensing water movement. Their whiskers extended forward ahead of the mouth. Seals performed rhythmic whisker movement to search for hydrodynamic cues, a whisker movement homologous to terrestrial mammals exploring their environment. Based on direct observations, we show how deep-diving seals locate their prey without the biosonar used by whales, revealing another mammalian adaptation to complete darkness.

The darkness of the deep ocean limits the vision of diving predators, except when prey emit bioluminescence. It is hypothesized that deep-diving seals rely on highly developed whiskers to locate their prey. However, if and how seals use their whiskers while foraging in natural conditions remains unknown. We used animal-borne tags to show that free-ranging elephant seals use their whiskers for hydrodynamic prey sensing. Small, cheek-mounted video loggers documented seals actively protracting their whiskers in front of their mouths with rhythmic whisker movement, like terrestrial mammals exploring their environment. Seals focused their sensing effort at deep foraging depths, performing prolonged whisker protraction to detect, pursue, and capture prey. Feeding-event recorders with light sensors demonstrated that bioluminescence contributed to only about 20% of overall foraging success, confirming that whiskers play the primary role in sensing prey. Accordingly, visual prey detection complemented and enhanced prey capture. The whiskers’ role highlights an evolutionary alternative to echolocation for adapting to the extreme dark of the deep ocean environment, revealing how sensory abilities shape foraging niche segregation in deep-diving mammals. Mammals typically have mobile facial whiskers, and our study reveals the significant function of whiskers in the natural foraging behavior of a marine predator. We demonstrate the importance of field-based sensory studies incorporating multimodality to better understand how multiple sensory systems are complementary in shaping the foraging success of predators.

Effects of a parasympathetic blocker on the heart rate of loggerhead turtles during voluntary diving

Diving bradycardia is a reduction in the heart rate mediated by the parasympathetic system during diving. Although diving bradycardia is pronounced in aquatic mammals and birds, the existence of this response in aquatic reptiles, including sea turtles, remains under debate. Using the parasympathetic blocker atropine, we evaluated the involvement of the parasympathetic nervous system in heart rate reduction of loggerhead sea turtles (Caretta caretta) during voluntary diving in tanks. The heart rate of the control group dropped by 40-60 % from the pre-dive value at the onset of diving; however, administration of atropine significantly inhibited heart rate reduction (P<0.001). Our results indicate that, similar to mammals and birds, the heart rate reduction in sea turtles while diving is primarily mediated by the parasympathetic nervous system. In conclusion, we suggest that diving bradycardia exists not only in aquatic mammals and birds but also in aquatic reptiles.

sspamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Light‐level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large‐scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights.

Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k‐means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events).

Finally, we highlight some of the limitations, implications and opportunities of using these methods.

Life in the slow lane: Field Metabolic Rate and Prey Consumption Rate of the Greenland Shark (Somniosus microcephalus) modeled using Archival Biologgers

Field metabolic rate (FMR) is a holistic measure of metabolism representing the routine energy utilization of a species living within a specific ecological context, thus providing insight into its ecology, fitness and resilience to environmental stressors. For animals which cannot be easily observed in the wild, FMR can also be used in concert with dietary data to quantitatively assess their role as consumers, improving understanding of the trophic linkages that structure food webs and allowing for informed management decisions. Here we modeled the FMR of Greenland sharks (Somniosus microcephalus) equipped with biologger packages or pop-up archival satellite tags (PSATs) in two coastal inlets of Baffin Island (Nunavut) using metabolic scaling relationships for mass, temperature and activity. We estimated that Greenland sharks had an overall mean FMR of 21.67±2.30 mgO2h-1kg-0.84 (n=30; 1-4 day accelerometer package deployments) while residing inside these cold-water fjord systems in the late summer, and 25.48±0.47 mgO2h-1kg-0.84 (n=6; PSATs) over an entire year. When considering prey consumption rate, an average shark in these systems (224kg) requires a maintenance ration of 61-193g of fish or marine mammal prey daily. As a lethargic polar species, these low FMR estimates, and corresponding prey consumption estimates suggest Greenland sharks require very little energy to sustain themselves under natural conditions. These data provide the first characterization of the energetics and consumer role of this vulnerable and understudied species in the wild, essential given growing pressures from climate change and expanding commercial fisheries in the Arctic.

Heart rate as a proxy for estimating oxygen consumption rates in loggerhead turtles (Caretta caretta)

Heart rates of air-breathing diving animals can change on a short time scale due to the diving response during submergence. Heart rate is used frequently as a proxy for indirectly estimating metabolic rates on a fine time scale. However, most studies to date have been conducted on endothermic diving animals, and the relationships between metabolic rates and heart rates in ectothermic diving animals have not been well studied. Sea turtles are unique model organisms of diving ectotherms because they spend most of their life in the ocean and perform deep and/or long dives. In this study, we examined the relationship between heart rates and metabolic rates in captive loggerhead turtles, Caretta caretta, to estimate oxygen consumption rates during each dive based on heart rates. The oxygen consumption rates (V̇_O2: mlO₂ min⁻¹ kg⁻¹) and average heart rates (f_H: beats min⁻¹) were measured simultaneously in indoor tanks at water temperatures of 15–25°C. Our results showed that oxygen consumption rate was affected by heart rate and water temperature in loggerhead turtles. Based on the collected data, we formulated the model equation as V̇_O2=0.0124f_H+0.0047T_w - 0.0791. The equation can be used for estimating fine-scaled field metabolic rates in free-ranging loggerhead turtles. The results of this study will contribute to future comparative studies of the physiological states of ectothermic diving animals.

Summary: The relationship between oxygen consumption rate and heart rate in the loggerhead turtle.

A Non-Invasive Heart Rate Measurement Method Is Improved by Placing the Electrodes on the Ventral Side Rather Than the Dorsal in Loggerhead Turtles

Heart rate measurement is an essential method for evaluating the physiological status of air-breathing diving animals. However, owing to technical difficulties, many marine animals require an invasive approach to record an electrocardiogram (ECG) in water, limiting the application of this approach in a wide range of marine animals. Recently, a non-invasive system was reported to measure the ECG of hard-shelled sea turtles by pasting the electrodes on the dorsal side of the shell, although the ECG obtained from the moving turtle contains noise produced by muscle contraction. Here, we report that clear ECGs can be obtained by placing the electrodes on the ventral side rather than the dorsal side in loggerhead sea turtles. Using our method, clearer ECG signals were obtained with less electrical noise, even when turtles are swimming. According to the anatomical features, the electrode position on the ventral side is closer to the heart than the dorsal side, minimizing the effects of noise generated by the skeletal muscle. This new biologging technique will elucidate the functioning of the circulatory system of sea turtles during swimming and their adaptabilities to marine environments. This article is part of the theme issue “Methods and Applications in Physio-logging.”

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Behavioural response of a whale shark during the passage of a typhoon

During a behavioural survey of a tagged whale shark (Rhincodon typus) conducted in 2019 in the waters off Kagoshima, Japan, a typhoon passed close to the area under surveillance. As the typhoon approached, monitoring of the shark's movements indicated that it dived to depths of up to 90 m, and during this period, the authors recorded the effects of the typhoon-induced waves. They also detected changes in the vertical thermal structure of the waters, possibly due to the disturbance caused by the typhoon.

How often should dead-reckoned animal movement paths be corrected for drift?

BACKGROUND

Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, 'GPS') is typically used to verify an animal's location periodically. Straight lines are typically drawn between these 'Verified Positions' ('VPs') so the interpolation of space-use is limited by the temporal and spatial resolution of the system's measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.

METHODS AND RESULTS

Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.

CONCLUSIONS

We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

Mare incognita: Adélie penguins foraging in newly exposed habitat after calving of the Nansen Ice Shelf

Rapid environmental changes can dramatically and durably affect the animal's foraging behavior. In the Ross Sea (Antarctica), calving of the Nansen Ice Shelf in 2016 opened a newly accessible marine area of 214 km². In this study, we examined the foraging behavior of Adélie penguins from the nearby Inexpressible Island in December 2018, by tracking 27 penguins during their at-sea trips using GPS, depth and video loggers. The penguins mainly foraged within 88.2 ± 42.9 km of their colony, for 23.4 ± 6.8 h. Five penguins headed south to the newly exposed habitat along the Nansen Ice Shelf, whereas 22 penguins exploited previously available foraging areas. There was no significant difference in any of the foraging trip or diving parameters between the two penguin groups; however, in the calved region the penguins were diving into shallow areas more often than did the other penguins. These results show that Adélie penguins on Inexpressible Island had explored the newly exposed area after calving. We conclude that the penguins respond to newly available habitat following stochastic environmental events, either through information sharing at the colony, and/or by balancing prey availability per capita across the foraging sites. Considering that this penguin breeding area is under investigation for the establishment of an Antarctic Specially Protected Area (ASPA), the results of this study may provide insights for evaluating the ecological importance of this area and formulating an ASPA management plan for conservation.

Connecting post-release mortality to the physiological stress response of large coastal sharks in a commercial longline fishery

Bycatch mortality is a major factor contributing to shark population declines. Post-release mortality (PRM) is particularly difficult to quantify, limiting the accuracy of stock assessments. We paired blood-stress physiology with animal-borne accelerometers to quantify PRM rates of sharks caught in a commercial bottom longline fishery. Blood was sampled from the same individuals that were tagged, providing direct correlation between stress physiology and animal fate for sandbar (Carcharhinus plumbeus, N = 130), blacktip (C. limbatus, N = 105), tiger (Galeocerdo cuvier, N = 52), spinner (C. brevipinna, N = 14), and bull sharks (C. leucas, N = 14). PRM rates ranged from 2% and 3% PRM in tiger and sandbar sharks to 42% and 71% PRM in blacktip and spinner sharks, respectively. Decision trees based on blood values predicted mortality with >67% accuracy in blacktip and spinner sharks, and >99% accuracy in sandbar sharks. Ninety percent of PRM occurred within 5 h after release and 59% within 2 h. Blood physiology indicated that PRM was primarily associated with acidosis and increases in plasma potassium levels. Total fishing mortality reached 62% for blacktip and 89% for spinner sharks, which may be under-estimates given that some soak times were shortened to focus on PRM. Our findings suggest that no-take regulations may be beneficial for sandbar, tiger, and bull sharks, but less effective for more susceptible species such as blacktip and spinner sharks.

Using Machine Learning for Remote Behaviour Classification-Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.

A non-invasive system to measure heart rate in hard-shelled sea turtles: potential for field applications

To measure the heart rate of unrestrained sea turtles, it has been believed that a probe must be inserted inside the body owing to the presence of the shell. However, inserting the probe is invasive and difficult to apply to animals in the field. Here, we have developed a non-invasive heart rate measurement method for some species of sea turtles. In our approach, an electrocardiogram (ECG) was performed using an animal-borne ECG recorder and two electrodes-which were electrically insulated from seawater-pasted on the carapace. Based on the measured ECG, the heartbeat signals were identified with an algorithm using a band-pass filter. We implemented this algorithm in a user-friendly program package, ECGtoHR. In experiments conducted in a water tank and in a lagoon, we successfully measured the heart rate of loggerhead, olive ridley and black turtles, but not green and hawksbill turtles. The average heart rate of turtles when resting underwater was 6.2 ± 1.9 beats min-1 and that when moving at the surface was 14.0 ± 2.4 beats min-1. Our approach is particularly suitable for endangered species such as sea turtles, and has the potential to be extended to a variety of other free-ranging species. This article is part of the theme issue 'Measuring physiology in free-living animals (Part I)'.

Video and acceleration records of streaked shearwaters allows detection of two foraging behaviours associated with large marine predators

The study of seabird behaviour has largely relied on animal-borne tags to gather information, requiring interpretation to estimate at-sea behaviours. Details of shallow-diving birds’ foraging are less known than deep-diving species due to difficulty in identifying shallow dives from biologging devices. Development of smaller video loggers allow a direct view of these birds’ behaviours, at the cost of short battery capacity. However, recordings from video loggers combined with relatively low power usage accelerometers give a means to develop a reliable foraging detection method. Combined video and acceleration loggers were attached to streaked shearwaters in Funakoshi-Ohshima Island (39°24’N,141°59’E) during the breeding season in 2018. Video recordings were classified into behavioural categories (rest, transit, and foraging) and a detection method was generated from the acceleration signals. Two foraging behaviours, surface seizing and foraging dives, are reported with video recordings. Surface seizing was comprised of successive take-offs and landings (mean duration 0.6 and 1.5s, respectively), while foraging dives were shallow subsurface dives (3.2s mean duration) from the air and water surface. Birds were observed foraging close to marine predators, including dolphins and large fish. Results of the behaviour detection method were validated against video recordings, with mean true and false positive rates of 90% and 0%, 79% and 5%, and 66% and <1%, for flight, surface seizing, and foraging dives, respectively. The detection method was applied to longer duration acceleration and GPS datasets collected during the 2018 and 2019 breeding seasons. Foraging trips lasted between 1 − 8 days, with birds performing on average 16 surface seizing events and 43 foraging dives per day, comprising <1% of daily activity, while transit and rest took up 55 and 40%, respectively. This foraging detection method can address the difficulties of recording shallow-diving foraging behaviour and provides a means to measure activity budgets across shallow diving seabird species.

Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish

BACKGROUND

Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many "burst" behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different "burst" behaviours occurring naturally, where direct observations are not possible.

METHODS

We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables.

RESULTS

Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F₁ scores ranged from 0.48 (chafe) - 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration.

CONCLUSION

Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.

Exhausted with foraging: Foraging behavior is related to oxidative stress in chick-rearing seabirds

To understand foraging strategies and behavioral flexibility in wild animals, it is important to evaluate the physiological costs imposed by foraging efforts and how these costs affect foraging and provisioning behavior. Oxidative stress is a possible physiological indicator associated with foraging behavior in wild seabirds, and may also affect their reproductive performance. However, no previous study has simultaneously recorded foraging behavior and the associated oxidative stress in wild seabirds. Using an integrative approach based on oxidative stress measurements and bio-logging techniques (i.e., the use of animal-borne sensors), we determined the relationships between foraging behavior and oxidative stress in chick-rearing streaked shearwaters Calonectris leucomelas in 2018 and 2019. To quantify their oxidative stress, we measured reactive oxygen metabolites (d-ROMs) and biological antioxidant potential (BAP) in their plasma. We found that the d-ROMs levels were positively related to the maximum distance from the colony and the number of takeoffs, especially in 2019 when shearwaters flew further to forage. In 2018, when they flew relatively short distances, the BAP levels were positively related to the levels of their physical activity (overall dynamic body acceleration; ODBA). We conclude that longer and less successful foraging may lead to increase oxidative stress, while successful foraging may mitigate the oxidative stress of foraging by providing dietary antioxidants. Our results highlight that the combined data from bio-logging and oxidative stress measurements aid in evaluating the underlying physiological costs of foraging behavior in wild animals.

Influence of hunting strategy on foraging efficiency in Galapagos sea lions

The endangered Galapagos sea lion (GSL, Zalophus wollebaeki) exhibits a range of foraging strategies utilising various dive types including benthic, epipelagic and mesopelagic dives. In the present study, potential prey captures (PPC), prey energy consumption and energy expenditure in lactating adult female GSLs (n = 9) were examined to determine their foraging efficiency relative to the foraging strategy used. Individuals displayed four dive types: (a) epipelagic (<100 m; EP); or (b) mesopelagic (>100 m; MP) with a characteristic V-shape or U-shape diving profile; and (c) shallow benthic (<100 m; SB) or (d) deep benthic (>100 m; DB) with square or flat-bottom dive profiles. These dive types varied in the number of PPC, assumed prey types, and the energy expended. Prey items and their energetic value were assumed from previous GSL diet studies in combination with common habitat and depth ranges of the prey. In comparison to pelagic dives occurring at similar depths, when diving benthically, GSLs had both higher prey energy consumption and foraging energy expenditure whereas PPC rate was lower. Foraging efficiency varied across dive types, with benthic dives being more profitable than pelagic dives. Three foraging trip strategies were identified and varied relative to prey energy consumed, energy expended, and dive behaviour. Foraging efficiency did not significantly vary among the foraging trip strategies suggesting that, while individuals may diverge into different foraging habitats, they are optimal within them. These findings indicate that these three strategies will have different sensitivities to habitat-specific fluctuations due to environmental change.

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.

Accounting for body mass effects in the estimation of field metabolic rates from body acceleration

Dynamic body acceleration (DBA), measured through animal-attached tags, has emerged as a powerful method for estimating field metabolic rates of free-ranging individuals. Following respirometry to calibrate oxygen consumption rate (ṀO2) with DBA under controlled conditions, predictive models can be applied to DBA data collected from free-ranging individuals. However, laboratory calibrations are generally performed on a relatively narrow size range of animals, which may introduce biases if predictive models are applied to differently sized individuals in the field. Here, we tested the mass dependence of the ṀO2-DBA relationship to develop an experimental framework for the estimation of field metabolic rates when organisms differ in size. We performed respirometry experiments with individuals spanning one order of magnitude in body mass (1.74-17.15 kg) and used a two-stage modelling process to assess the intraspecific scale dependence of the ṀO2-DBA relationship and incorporate such dependencies into the coefficients of ṀO2 predictive models. The final predictive model showed scale dependence; the slope of the ṀO2-DBA relationship was strongly allometric (M1.55), whereas the intercept term scaled closer to isometry (M1.08). Using bootstrapping and simulations, we evaluated the performance of this coefficient-corrected model against commonly used methods of accounting for mass effects on the ṀO2-DBA relationship and found the lowest error and bias in the coefficient-corrected approach. The strong scale dependence of the ṀO2-DBA relationship indicates that caution must be exercised when models developed using one size class are applied to individuals of different sizes.

An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

BACKGROUND

Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission.

METHODS

We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus).

RESULTS

Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers.

CONCLUSIONS

Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation

Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7-91.0%/75.2-76.7%; rumination: 89.6-91.6%/53.5-86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes.

Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

BACKGROUND

Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective  classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.

METHODS

We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: 'flapping flight', 'soaring flight', and 'on-water'. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.

RESULTS

HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for 'flapping flight', 'soaring flight' and 'on-water', respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.

CONCLUSIONS

The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Inter-Specific and Intra-Specific Competition of Two Sympatrically Breeding Seabirds, Chinstrap and Gentoo Penguins, at Two Neighboring Colonies

Theory predicts that sympatric predators compete for food under conditions of limited resources. Competition would occur even within the same species, between neighboring populations, because of overlapping foraging habits. Thus, neighboring populations of the same species are hypothesized to face strong competition. To test the hypothesis that intra-specific competition is more intense than inter-specific competition owing to a lack of niche partitioning, we estimated the foraging area and diving depths of two colonial seabird species at two neighboring colonies. Using GPS and time-depth recorders, we tracked foraging space use of sympatric breeding Chinstrap and Gentoo penguins at Ardley Island (AI) and Narębski Point (NP) at King George Island, Antarctica. GPS tracks showed that there was a larger overlap in the foraging areas between the two species than within each species. In dive parameters, Gentoo penguins performed deeper and longer dives than Chinstrap penguins at the same colonies. At the colony level, Gentoo penguins from NP undertook deeper and longer dives than those at AI, whereas Chinstrap penguins did not show such intra-specific differences in dives. Stable isotope analysis of δ¹³C and δ¹⁵N isotopes in blood demonstrated both inter- and intra-specific differences. Both species of penguin at AI exhibited higher δ¹³C and δ¹⁵N values than those at NP, and in both locations, Gentoo penguins had higher δ¹³C and lower δ¹⁵N values than Chinstrap penguins. Isotopic niches showed that there were lower inter-specific overlaps than intra-specific overlaps. This suggests that, despite the low intra-specific spatial overlap, diets of conspecifics from different colonies remained more similar, resulting in the higher isotopic niche overlaps. Collectively, our results support the hypothesis that intra-specific competition is higher than inter-specific competition, leading to spatial segregation of the neighboring populations of the same species.

Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers

Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine learning methods are commonly applied to animal accelerometry data; however, they require the trial of multiple methods to find an ideal solution. We used tri-axial accelerometers (10 Hz) to quantify four behaviours in Port Jackson sharks (Heterodontus portusjacksoni): two fine-scale behaviours (<2 s)-(1) vertical swimming and (2) chewing as proxy for foraging, and two broad-scale behaviours (>2 s-mins)-(3) resting and (4) swimming. We used validated data to calculate 66 summary statistics from tri-axial accelerometry and assessed the most important features that allowed for differentiation between the behaviours. One and two second epoch testing sets were created consisting of 10 and 20 samples from each behaviour event, respectively. We developed eight machine learning models to assess their overall accuracy and behaviour-specific accuracy (one classification tree, five ensemble learners and two neural networks). The support vector machine model classified the four behaviours better when using the longer 2 s time epoch (F-measure 89%; macro-averaged F-measure: 90%). Here, we show that this support vector machine (SVM) model can reliably classify both fine- and broad-scale behaviours in Port Jackson sharks.

Foraging niche overlap during chick-rearing in the sexually dimorphic Westland petrel

Most Procellariform seabirds are pelagic, breed in summer when prey availability peaks, and migrate for winter. They also display a dual foraging strategy (short and long trips) and sex-specific foraging. The Westland petrel Procellaria westlandica, a New Zealand endemic, is one of the rare seabirds breeding in winter. Preliminary findings on this large and sexually dimorphic petrel suggest a foraging behaviour with no evidence of a dual strategy, within a narrow range and with shared areas between sexes. To investigate further this unusual strategy, the present study determined the fine-scale at-sea behaviours (global positioning system and accelerometer data loggers) and trophic niches (stable isotopes in whole blood) of chick-rearing individuals (16 males and 13 females). All individuals foraged on the shelf-slope of the west coast of New Zealand's South Island with short, unimodal trips. Both sexes foraged at similar intensity without temporal, spatial or isotopic niche segregation. These findings suggest the presence of a winter prey resource close to the colony, sufficient to satisfy the nutritional needs of breeding without increasing the foraging effort or intra-specific competition avoidance during winter. Additional data are needed to assess the consistency of foraging niche between the sexes and its reproductive outcomes in view of anticipated environmental changes.

Depth-dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks

Tiger sharks, Galeocerdo cuvier, are a keystone, top-order predator that are assumed to engage in cost-efficient movement and foraging patterns. To investigate the extent to which oscillatory diving by tiger sharks conform to these patterns, we used a biologging approach to model their cost of transport. High-resolution biologging tags with tri-axial sensors were deployed on 21 tiger sharks at Ningaloo Reef for durations of 5-48 h. Using overall dynamic body acceleration as a proxy for energy expenditure, we modelled the cost of transport of oscillatory movements of varying geometries in both horizontal and vertical planes for tiger sharks. The cost of horizontal transport was minimized by descending at the smallest possible angle and ascending at an angle of 5-14°, meaning that vertical oscillations conserved energy compared to swimming at a level depth. The reduction of vertical travel costs occurred at steeper angles. The absolute dive angles of tiger sharks increased between inshore and offshore zones, presumably to reduce the cost of transport while continuously hunting for prey in both benthic and surface habitats. Oscillatory movements of tiger sharks conform to strategies of cost-efficient foraging, and shallow inshore habitats appear to be an important habitat for both hunting prey and conserving energy while travelling.

Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures

Automated monitoring of the movements and behaviour of animals is a valuable research tool. Recently, machine learning tools were applied to many species to classify units of behaviour. For the monitoring of wild species, collecting enough data for training models might be problematic, thus we examine how machine learning models trained on one species can be applied to another closely related species with similar behavioural conformation. We contrast two ways to calculate accuracies, termed here as overall and threshold accuracy, because the field has yet to define solid standards for reporting and measuring classification performances. We measure 21 dogs and 7 wolves, and find that overall accuracies are between 51 and 60% for classifying 8 behaviours (lay, sit, stand, walk, trot, run, eat, drink) when training and testing data are from the same species and between 41 and 51% when training and testing is cross-species. We show that using data from dogs to predict the behaviour of wolves is feasible. We also show that optimising the model for overall accuracy leads to similar overall and threshold accuracies, while optimizing for threshold accuracy leads to threshold accuracies well above 80%, but yielding very low overall accuracies, often below the chance level. Moreover, we show that the most common method for dividing the data between training and testing data (random selection of test data) overestimates the accuracy of models when applied to data of new specimens. Consequently, we argue that for the most common goals of animal behaviour recognition overall accuracy should be the preferred metric. Considering, that often the goal is to collect movement data without other methods of observation, we argue that training data and testing data should be divided by individual and not randomly.

Multi-predator assemblages, dive type, bathymetry and sex influence foraging success and efficiency in African penguins

Marine predators adapt their hunting techniques to locate and capture prey in response to their surrounding environment. However, little is known about how certain strategies influence foraging success and efficiency. Due to the miniaturisation of animal tracking technologies, a single individual can be equipped with multiple data loggers to obtain multi-scale tracking information. With the addition of animal-borne video data loggers, it is possible to provide context-specific information for movement data obtained over the video recording periods. Through a combination of video data loggers, accelerometers, GPS and depth recorders, this study investigated the influence of habitat, sex and the presence of other predators on the foraging success and efficiency of the endangered African penguin, Spheniscus demersus, from two colonies in Algoa Bay, South Africa. Due to limitations in the battery life of video data loggers, a machine learning model was developed to detect prey captures across full foraging trips. The model was validated using prey capture signals detected in concurrently recording accelerometers and animal-borne cameras and was then applied to detect prey captures throughout the full foraging trip of each individual. Using GPS and bathymetry information to inform the position of dives, individuals were observed to perform both pelagic and benthic diving behaviour. Females were generally more successful on pelagic dives than males, suggesting a trade-off between manoeuvrability and physiological diving capacity. By contrast, males were more successful in benthic dives, at least for Bird Island (BI) birds, possibly due to their larger size compared to females, allowing them to exploit habitat deeper and for longer durations. Both males at BI and both sexes at St Croix (SC) exhibited similar benthic success rates. This may be due to the comparatively shallower seafloor around SC, which could increase the likelihood of females capturing prey on benthic dives. Observation of camera data indicated individuals regularly foraged with a range of other predators including penguins and other seabirds, predatory fish (sharks and tuna) and whales. The presence of other seabirds increased individual foraging success, while predatory fish reduced it, indicating competitive exclusion by larger heterospecifics. This study highlights novel benthic foraging strategies in African penguins and suggests that individuals could buffer the effects of changes to prey availability in response to climate change. Furthermore, although group foraging was prevalent in the present study, its influence on foraging success depends largely on the type of heterospecifics present.

Divergent field metabolic rates highlight the challenges of increasing temperatures and energy limitation in aquatic ectotherms

Environments where extreme temperatures and low productivity occur introduce energetically challenging circumstances that may be exacerbated by climate change. Despite the strong link between metabolism and temperature in ectotherms, there is a paucity of data regarding how the metabolic ecology of species affects growth and fitness under such circumstances. Here, we integrated data describing field metabolic rates and body condition of two sympatric species of ectotherms with divergent lifestyles, the benthic freshwater (or largetooth) sawfish (Pristis pristis) and the epipelagic bull shark (Carcharhinus leucas) occurring in the Fitzroy River, Western Australia, to test the implications of their differing metabolic ecologies for vulnerability to rising temperatures. Over a temperature range of 18-34 °C, sawfish had lower field metabolic rates (63-187 mg O₂ kg^-0.86 h^-1) and lower temperature sensitivity of metabolic rates [activation energy (E_A) = 0.35 eV] than bull sharks (187-506 mg O₂ kg^-0.86 h^-1; E_A = 0.48 eV). Both species lost body mass throughout the dry season, although bull sharks significantly more (0.17% mass loss day^-1) than sawfish (0.07% mass loss day^-1). Subsequent bioenergetics modelling showed that under future climate change scenarios, both species would reach potentially lethal levels of mass loss during dry season periods before the end of the century. These results suggest that ectotherms with low metabolic rates may be better suited to extreme environmental conditions, and that even small increases in temperature due to climate change could have substantial impacts on the ability of ectotherms to grow and survive in harsh conditions, including high temperatures and energy-limiting circumstances.

Parasitic mites alter chicken behaviour and negatively impact animal welfare

The northern fowl mite, Ornithonyssus sylviarum, is one of the most common and damaging ectoparasites of poultry. As an obligate blood feeding mite, the northern fowl mite can cause anaemia, slower growth, and decreased egg production of parasitized birds. However, the impact of mites or other ectoparasites on hen behaviour or welfare is not well studied. Here, we use activity sensors (three-axis accelerometers) affixed to individual birds to continuously record hen movement before, during, and after infestation with mites. Movements recorded by sensors were identified to specific bird behaviours through a previously trained algorithm, with frequency of these behaviours recorded for individual birds. Hen welfare was also determined before, during, and after mite infestation of hens using animal-based welfare metrics. Northern fowl mites significantly increased hen preening behaviour and resulted in increased skin lesions of infested birds.

Geographical, temporal and individual factors influencing foraging behaviour and consistency in Australasian gannets

Foraging is a behaviour that can be influenced by multiple factors and is highly plastic. Recent studies have shown consistency in individual foraging behaviour has serious ecological and evolutionary implications within species and populations. Such information is crucial to understand how species select habitats, and how such selection might allow them to adapt to the environmental changes they face. Five foraging metrics (maximum distance from the colony, bearing from the colony to the most distal point, tortuosity index, total number of dives and mean vectorial dynamic body acceleration were obtained using GPS tracking and accelerometry data in adult Australasian gannets (Morus serrator) from two colonies in southeastern Australia. Individuals were instrumented over two breeding seasons to obtain data to assess factors influencing foraging behaviour and behavioural consistency over multiple timescales (consecutive trips, breeding stages and years) and habitats (pelagic, mixed pelagic and inshore, and inshore). Colony, breeding stage and year were the factors which had the greatest influence on foraging behaviour, followed by sex. Behavioural consistency, measured as the contribution of the individual to the observed variance, was low to moderate for all foraging metrics (0.0-27.05%), with the higher values occurring over shorter timescales. In addition, behavioural consistency was driven by spatio-temporal factors rather than intrinsic characteristics. Behavioural consistency was higher in individuals foraging in inshore than pelagic habitats or mixed pelagic/inshore strategy, supporting suggestions that consistency is favoured in stable environments.

The assessment of mouse spontaneous locomotor activity using motion picture

Spontaneous locomotor activity (SLA) is a useful parameter reflecting physical and mental status of experimental animals. Here we aimed to establish a novel and simple method to assess mouse SLA using motion picture. Movement of C57BL/6 mice was continuously recorded by an infrared video camera connected with a single board computer. The geometric center of mouse outline in each frame was calculated using an image processing library, OpenCV in a programming language Python. Moving distance of the geometric center every second was utilized as an index of mouse SLA. Twenty-four hours assessment of SLA showed that mice repeated active and resting phase. Mice moved more actively during the dark period compared with the light period. Time-frequency analysis of SLA followed by unsupervised clustering classified their active and resting phase. Administration of a sedative, chlorpromazine (5 mg/kg) abolished mouse SLA for 8 h. In contrast, administration of a central nervous stimulant, caffeine (25 mg/kg) increased SLA for 3 h. In conclusion, we here established the automatic measurement system of mouse SLA using motion picture. This system is composed of common equipment and analysis software written in freely available programming language. We also confirmed that it is applicable for drug assessment.