Interpreting behaviors from accelerometry: a method combining simplicity and objectivity

Sex- and social context-dependent differences in mice fine head movement during social interactions

BACKGROUND

Social decision-making is influenced by multiple factors such as age, sex, emotional state, and the individual's social environment. While various behavioural readouts have been commonly used to study social behaviour in rodents, the role of fine head movements during social interactions remains underexplored despite the presence of accelerometers in many electrophysiological recording systems.

RESULTS

Here, we used head acceleration data to analyse head movement kinematics in adult male and female mice across several social discrimination tests in various time scales. Our findings demonstrate the complementary nature of two variables derived from the raw acceleration, namely overall static (OSHA) and dynamic (ODHA) head acceleration, as well as specific head angles (Pitch and Roll). Together, these variables provide a comprehensive, detailed analysis of head movement, which cannot be easily achieved by video analysis systems such as DeepLabCut. Overall, our results suggest that head movement patterns are significantly influenced by sex, stimulus preference, and social context. Specifically, ODHA exhibited strong sex dependence and appeared to be more sensitive to internal states such as arousal and alertness. The static components were primarily influenced by social context, particularly stimulus preference, and seemed to reflect the subject's motivation to engage with the stimulus. The Roll angle also appeared strongly modulated by the broader social context.

CONCLUSIONS

Our study provides a novel method and analysis pipeline for studying the social behaviour of small rodents in high-time resolution using a head-based accelerometer. Our findings suggest that such measurements may inform the affective and motivational states of the subject during social interactions.

Flight style and time-activity budgets of the smallest petrels

Procellariforms are the most oceanic among birds, regularly embarking on the longest journeys in the animal kingdom to find food over an apparently featureless sea surface. To minimize energy expenditure, many species harness wind energy through dynamic soaring, extracting kinetic energy from the wind shear. The smallest members of this order, storm petrels, have functional traits that prevent this type of locomotion, and are predicted to rely on flapping flight despite their high motility. However, theoretical predictions have never been validated and their flight strategy and activity budgets are unclear. We hypothesized that, as the benefits of dynamic soaring are out of reach, these birds should rely on gliding to some extent to sustain their long-ranging movements and save energy. To test our hypothesis we used, for the first time, miniaturized inertial measurement units on one of the world's smallest seabirds, the Mediterranean storm petrel (Hydrobates pelagicus melitensis). We demonstrate that these small seafarers spend 78% of the time flying during their foraging trips, with wingbeat frequencies up to 15 Hz. During transiting flight, they flap their wings at high frequency (mean±s.d.: 8.8±0.8 Hz) for 91% of the time, gliding sporadically and only for an instant (mean±s.d.: 0.11±0.17 s). Flight activity was high during the night and early morning, while they rested on the sea in the central hours of the day. Overall, our results reveal a peculiar locomotory strategy among procellariforms and raise questions about how they can energetically sustain foraging trips spanning hundreds of kilometres.

An annual cycle perspective on energetics and locomotion of migratory animals

Animal migrations, or long-distance movements, on land, through water or in the air, are considered energetically costly because of the investment in persistent locomotion typical for migration. Diverse strategies exist to manage these energetic costs. Yet migration is only one stage in an annual cycle and may not be the most energetically costly. To better understand how free-ranging animals adaptively organize energy expenditure and locomotion, an annual cycle perspective is needed. Bio-logging data are collected for a range of animal species and could facilitate a life cycle approach to study energy expenditure. We provide examples from several studies across different taxa, as well as a more in-depth exploration from our own recent research on time activity budgets based on field observations and bio-logging data to estimate daily energy expenditure in a migratory seabird throughout a year. Our research has shown that daily energy expenditure is highest (1.7× average daily energy expenditure) during the spring migration of long-distance migratory gulls, whereas short-distance migrants expend the most energy (1.4× average daily energy expenditure) during the breeding season. Based on the examples provided, we show how bio-energetic models create exciting opportunities to study daily energetics and behaviour of migratory animals, although limitations also still exist. Such studies can reveal when, where and why peaks and lulls in energy expenditure arise over the annual cycle of a migrant, if long-distance movements are indeed energetically expensive and how animals can adapt to fluctuating demands in their natural environment throughout the year.

Intraspecific scaling of home range size and its bioenergetic association

Home range size and metabolic rate of animals are theorized to scale in relation to body mass with similar exponents. This expectation has only been indirectly tested using lab-derived estimates of basal metabolic rate as proxies for field energy requirements. Therefore, it is unclear if existing theory aligns with observed patterns of home range scaling since field metabolic rates may scale differently than basal metabolic rates. We conducted the first direct field test of the relationship between home range and metabolic rate allometry. Using acoustic telemetry, we simultaneously measured the home range sizes and field metabolic rates of lemon sharks (Negaprion brevirostris) spanning one order of magnitude in body mass and compared the allometric scaling exponents of these traits. Similarity between allometric scaling exponents confirmed an expected strong association between metabolic rate and home range size. However, a nonsignificant but negative association between standard metabolic rate (SMR) and home range size suggests a complex relationship between metabolism and home range, contrasting previous assumptions of a positive relationship. Nevertheless, an overall positive association between home range size and total metabolic rate persisted, driven by a strong association between active energy expenditure and home range size. These findings underscore the intricate relationship between energetics and home range size, emphasizing the need for additional direct field investigations and the potential for modern tagging technologies to gather relevant data.

Time-energy budgets outperform dynamic body acceleration in predicting daily energy expenditure in kittiwakes, and estimate a very low cost of gliding flight relative to flapping flight

Energy is a common currency for any living organism, yet estimating energy expenditure in wild animals is challenging. Accelerometers are commonly used to estimate energy expenditure, via a dynamic body acceleration (DBA) or time-energy budget approach. The DBA approach estimates energy expenditure directly from acceleration but may lead to erroneous estimates during inactivity when acceleration is zero but energy expenditure is not. The time-energy budget approach uses accelerometers and other data streams to assign a behaviour to each time step, and then calculates energy expenditure based on activity-specific metabolic rates assigned to each behaviour. Here, we used GPS-accelerometry in breeding black-legged kittiwakes (Rissa tridactyla, n=80) to calculate DBA and time-energy budgets derived from simple biologging metrics (speed, wingbeat frequency, GPS position). We then compared these two approaches with estimates of energy expenditure from doubly labelled water (DLW). Energy expenditure estimated from DLW correlated with DBA, but the best model to estimate energy expenditure was based on time-energy budgets. Energetic costs of flapping flight were higher than all other kittiwake behaviours (5.54×basal metabolic rate, BMR). Energetic costs of gliding flight (0.80×BMR) were the lowest of all behaviours, and equivalent to the cost of resting at the colony. DEE for our birds estimated from our calibration coefficients was similar to DEE for our birds estimated with the model coefficient published using different methods. We conclude that once calibrated with DLW, GPS-accelerometry provides a simple method for measuring energy expenditure in wild kittiwakes based on time-energy budgets.

Handedness and individual roll-angle specialism when plunge diving in the northern gannet

Many vertebrates show lateralized behaviour, or handedness, where an individual preferentially uses one side of the body more than the other. This is generally thought to be caused by brain lateralization and allows functional specializations such as sight, locomotion, and decision-making among other things. We deployed accelerometers on 51 northern gannets, Morus bassanus, to test for behavioural lateralization during plunge dives. When plunge diving, gannets 'roll' to one side, and standard indices indicated that 51% of individuals were left-sided, 43% right-sided, and 6% 'non-lateralized'. Lateralization indices provide no measure of error and do not account for environmental covariance, so we conducted two repeatability analyses on individuals' dive roll direction and angle. Dive side lateralization was highly repeatable among individuals over time at the population level (R = 0.878, p < 0.001). Furthermore, roll angle was also highly repeatable in individuals (R = 0.751, p < 0.001) even after controlling for lateralized state. Gannets show individual specializations in two different parts of the plunge diving process when attempting to catch prey. This is the first demonstration of lateralization during prey capture in a foraging seabird. It is also one of the few demonstrations of behavioural lateralization in a mixed model approach, providing a structure for further exploring behavioural lateralization.

"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.

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.

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

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

Variety is the spice of life: Flying-foxes exploit a variety of native and exotic food plants in an urban landscape mosaic

Generally, urbanization is a major threat to biodiversity; however, urban areas also provide habitats that some species can exploit. Flying-foxes (Pteropus spp.) are becoming increasingly urbanized; which is thought to be a result of increased availability and temporal stability of urban food resources, diminished natural food resources, or both. Previous research has shown that urban-roosting grey-headed flying-foxes (Pteropus poliocephalus) preferentially forage in human-modified landscapes. However, which land-use areas and food plants support its presence in urban areas is unknown. We tracked nine P. poliocephalus roosting in Adelaide, South Australia, between December 2019 and May 2020, using global positioning systems (GPS), to investigate how individuals used the urban landscape mosaic for feeding. The most frequently visited land-use category was “residential” (40% of fixes) followed by “road-side,” “reserves” and “primary production” (13–14% each). However, “reserves” were visited four times more frequently than expected from their areal availability, followed by the “residential” and “road-side” categories that were visited approximately twice more than expected each; in contrast, the “primary production” category was visited approximately five times less than expected. These results suggest that while residential areas provide most foraging resources supporting Adelaide’s flying-fox population, reserves contain foraging resources that are particularly attractive to P. poliocephalus. Primary production land was relatively less utilized, presumably because it contains few food resources. Throughout, flying-foxes visited an eclectic mixture of diet plants (49 unique species), with a majority of feeding fixes (63%) to locally indigenous Australian native species; however, in residential areas 53% of feeding visits were to non-locally indigenous species, vs only 13% in reserves. Flowering and fruiting phenology records of the food plants visited further indicated that non-locally indigenous species increase the temporal availability of foraging resources for P. poliocephalus in urban Adelaide. Our findings demonstrate the importance of residential areas for urban-roosting P. poliocephalus, and suggest that the anthropogenic mixture of food resources available in the urban landscape mosaic supports the species’ year-round presence in urban areas. Our results further highlight the importance of conserving natural habitats within the urban landscape mosaic, and stress the need for accounting for wildlife responses to urban greening initiatives.

A bioenergetics approach to understanding sex differences in the foraging behaviour of a sexually monomorphic species

Many animals show sexually divergent foraging behaviours reflecting different physiological constraints or energetic needs. We used a bioenergetics approach to examine sex differences in foraging behaviour of the sexually monomorphic northern gannet. We derived a relationship between dynamic body acceleration and energy expenditure to quantify the energetic cost of prey capture attempts (plunge dives). Fourteen gannets were tracked using GPS, time depth recorders (TDR) and accelerometers. All plunge dives in a foraging trip represented less than 4% of total energy expenditure, with no significant sex differences in expenditure. Despite females undertaking significantly more dives than males, this low energetic cost resulted in no sex differences in overall energy expenditure across a foraging trip. Bayesian stable isotope mixing models based on blood samples highlighted sex differences in diet; however, calorific intake from successful prey capture was estimated to be similar between sexes. Females experienced 10.28% higher energy demands, primarily due to unequal chick provisioning. Estimates show a minimum of 19% of dives have to be successful for females to meet their daily energy requirements, and 26% for males. Our analyses suggest northern gannets show sex differences in foraging behaviour primarily related to dive rate and success rather than the energetic cost of foraging or energetic content of prey.

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.

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.

Overland and oversea migration of white storks through the water barriers of the straits of Gibraltar

Soaring landbirds typically exploit atmospheric uplift as they fly overland, displaying a highly effective energy-saving locomotion. However, large water bodies lack thermal updrafts, potentially becoming ecological barriers that hamper migration. Here we assessed the effects of a sea surface on the migratory performance of GPS-tagged white storks (Ciconia ciconia) before, during and after they crossed the straits of Gibraltar. Oversea movements involved only flapping and gliding and were faster, traversed in straighter, descending trajectories and resulted in higher movement-related energy expenditure levels than overland, supporting the water barrier hypothesis. Overland movements at both sides of the sea straits resulted in tortuous routes and ascending trajectories with pre-crossing flights showing higher elevations and more tortuous routes than post-crossing, thus supporting the barrier negotiation hypothesis. Individual positions at both ends of the sea narrow were predicted by zonal winds and storks´ location at entry in the European hinterland, and birds did not show compensational movements overland in anticipation to subsequent wind displacements oversea. The length of the water narrow at departure shore, the elevation therein and the winds on route affected major components of sea crossing performance (such as distances and times overwater, minimum elevations, climb angles, speeds and energy expenditure), supporting the departure position and oversea winds hypotheses. In summary, our study provides a prime example at high temporal resolution of how birds adjust their behavior and physiology as they interact with the changing conditions of the travelling medium, reallocating resources and modifying their movement to overcome an ecological barrier.

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.

A new direction for differentiating animal activity based on measuring angular velocity about the yaw axis

The use of animal-attached data loggers to quantify animal movement has increased in popularity and application in recent years. High-resolution tri-axial acceleration and magnetometry measurements have been fundamental in elucidating fine-scale animal movements, providing information on posture, traveling speed, energy expenditure, and associated behavioral patterns. Heading is a key variable obtained from the tandem use of magnetometers and accelerometers, although few field investigations have explored fine-scale changes in heading to elucidate differences in animal activity (beyond the notable exceptions of dead-reckoning).This paper provides an overview of the value and use of animal heading and a prime derivative, angular velocity about the yaw axis, as an important element for assessing activity extent with potential to allude to behaviors, using "free-ranging" Loggerhead turtles (Caretta caretta) as a model species.We also demonstrate the value of yaw rotation for assessing activity extent, which varies over the time scales considered and show that various scales of body rotation, particularly rate of change of yaw, can help resolve differences between fine-scale behavior-specific movements. For example, oscillating yaw movements about a central point of the body's arc implies bouts of foraging, while unusual circling behavior, indicative of conspecific interactions, could be identified from complete revolutions of the longitudinal axis.We believe this approach should help identification of behaviors and "space-state" approaches to enhance our interpretation of behavior-based movements, particularly in scenarios where acceleration metrics have limited value, such as for slow-moving animals.

Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours

1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. 4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82), SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the credibility of the output. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

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

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

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

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

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

Simple Summary

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

Abstract

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

The energetic cost of parasitism in a wild population

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

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick-billed murres (Uria lomvia) and black-legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri-axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird behavior and ecology.

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

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

Utility of time-lapse photography in studies of seabird ecology

Marine ecosystems are heavily influenced by a wide range of human-related impacts, and thus monitoring is essential to preserve and manage these sensitive habitats. Seabirds are considered important bioindicators of the oceans, but accessing breeding populations can be difficult, expensive and time consuming. New technologies have been employed to facilitate data collection on seabirds that can reduce costs and minimize disturbance. Among these, the use of time-lapse photography is a potentially effective way to reduce researcher effort, while collecting valuable information on key ecological parameters. However, the feasibility of this approach remains uncertain. Here, we assessed the use of time-lapse photography as a tool for estimating foraging behaviour from breeding seabirds, and evaluate ways forward for this method. We deployed cameras in front of active nests at a colony of black-legged kittiwakes (Rissa tridactyla) during two breeding seasons, 5 nests in 2013 and 5 in 2014, taking pictures every 4 minutes. A subsample of monitored individuals were also equipped with accelerometers. Approximately 100,000 frames, covering incubation and chick-rearing periods, were analysed. Estimates of foraging trip duration from images were positively correlated with accelerometry estimates (R² = 0.967). Equal partitioning of effort between pairs, predation events, nest attendance patterns and variation in trip metrics with breeding stage were also identified. Our results suggest that time-lapse photography is potentially a useful tool for assessing foraging trip duration and other fine-scale nesting ecology parameters as well as for assessing the effect of bio-logging devices on seabird foraging behaviour. Nevertheless, the time investment to manually extract data from images was high, and the process to set up cameras was not straightforward. To encourage wide use of time-lapse photography in seabird ecology, we thus provide guidelines for camera deployment and we suggest a need for further development of automated approaches to allow data extraction.

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

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

Validating accelerometry estimates of energy expenditure across behaviours using heart rate data in a free-living seabird

Two main techniques have dominated the field of ecological energetics: the heart rate and doubly labelled water methods. Although well established, they are not without their weaknesses, namely expense, intrusiveness and lack of temporal resolution. A new technique has been developed using accelerometers; it uses the overall dynamic body acceleration (ODBA) of an animal as a calibrated proxy for energy expenditure. This method provides high-resolution data without the need for surgery. Significant relationships exist between the rate of oxygen consumption (V̇O2 ) and ODBA in controlled conditions across a number of taxa; however, it is not known whether ODBA represents a robust proxy for energy expenditure consistently in all natural behaviours and there have been specific questions over its validity during diving, in diving endotherms. Here, we simultaneously deployed accelerometers and heart rate loggers in a wild population of European shags (Phalacrocorax aristotelis). Existing calibration relationships were then used to make behaviour-specific estimates of energy expenditure for each of these two techniques. Compared with heart rate-derived estimates, the ODBA method predicts energy expenditure well during flight and diving behaviour, but overestimates the cost of resting behaviour. We then combined these two datasets to generate a new calibration relationship between ODBA and V̇O2  that accounts for this by being informed by heart rate-derived estimates. Across behaviours we found a good relationship between ODBA and V̇O2 Within individual behaviours, we found useable relationships between ODBA and V̇O2  for flight and resting, and a poor relationship during diving. The error associated with these new calibration relationships mostly originates from the previous heart rate calibration rather than the error associated with the ODBA method. The equations provide tools for understanding how energy constrains ecology across the complex behaviour of free-living diving birds.

Actogram analysis of free-flying migratory birds: new perspectives based on acceleration logging

The use of accelerometers has become an important part of biologging techniques for large-sized birds with accelerometer data providing information about flight mode, wing-beat pattern, behaviour and energy expenditure. Such data show that birds using much energy-saving soaring/gliding flight like frigatebirds and swifts can stay airborne without landing for several months. Successful accelerometer studies have recently been conducted also for free-flying small songbirds during their entire annual cycle. Here we review the principles and possibilities for accelerometer studies in bird migration. We use the first annual actograms (for red-backed shrike Lanius collurio) to explore new analyses and insights that become possible with accelerometer data. Actogram data allow precise estimates of numbers of flights, flight durations as well as departure/landing times during the annual cycle. Annual and diurnal rhythms of migratory flights, as well as prolonged nocturnal flights across desert barriers are illustrated. The shifting balance between flight, rest and different intensities of activity throughout the year as revealed by actogram data can be used to analyse exertion levels during different phases of the life cycle. Accelerometer recording of the annual activity patterns of individual birds will open up a new dimension in bird migration research.

Wireless inertial measurement of head kinematics in freely-moving rats

While miniature inertial sensors offer a promising means for precisely detecting, quantifying and classifying animal behaviors, versatile inertial sensing devices adapted for small, freely-moving laboratory animals are still lacking. We developed a standalone and cost-effective platform for performing high-rate wireless inertial measurements of head movements in rats. Our system is designed to enable real-time bidirectional communication between the headborne inertial sensing device and third party systems, which can be used for precise data timestamping and low-latency motion-triggered applications. We illustrate the usefulness of our system in diverse experimental situations. We show that our system can be used for precisely quantifying motor responses evoked by external stimuli, for characterizing head kinematics during normal behavior and for monitoring head posture under normal and pathological conditions obtained using unilateral vestibular lesions. We also introduce and validate a novel method for automatically quantifying behavioral freezing during Pavlovian fear conditioning experiments, which offers superior performance in terms of precision, temporal resolution and efficiency. Thus, this system precisely acquires movement information in freely-moving animals, and can enable objective and quantitative behavioral scoring methods in a wide variety of experimental situations.

Interpreting behaviors from accelerometry: a method combining simplicity and objectivity

Quantifying the behavior of motile, free-ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study-specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse-scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.