Inactivity/sleep in two wild free-roaming African elephant matriarchs - Does large body size make elephants the shortest mammalian sleepers?

The current study provides details of sleep (or inactivity) in two wild, free-roaming African elephant matriarchs studied in their natural habitat with remote monitoring using an actiwatch subcutaneously implanted in the trunk, a standard elephant collar equipped with a GPS system and gyroscope, and a portable weather station. We found that these two elephants were polyphasic sleepers, had an average daily total sleep time of 2 h, mostly between 02:00 and 06:00, and displayed the shortest daily sleep time of any mammal recorded to date. Moreover, these two elephants exhibited both standing and recumbent sleep, but only exhibited recumbent sleep every third or fourth day, potentially limiting their ability to enter REM sleep on a daily basis. In addition, we observed on five occasions that the elephants went without sleep for up to 46 h and traversed around 30 km in 10 h, possibly due to disturbances such as potential predation or poaching events, or a bull elephant in musth. They exhibited no form of sleep rebound following a night without sleep. Environmental conditions, especially ambient air temperature and relative humidity, analysed as wet-bulb globe temperature, reliably predict sleep onset and offset times. The elephants selected novel sleep sites each night and the amount of activity between sleep periods did not affect the amount of sleep. A number of similarities and differences to studies of elephant sleep in captivity are noted, and specific factors shaping sleep architecture in elephants, on various temporal scales, are discussed

Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events

Wild animals' biologging through machine learning models

In recent decades the biodiversity crisis has been characterised by a decline and extinction of many animal species worldwide. To aid in understanding the threats and causes of this demise, conservation scientists rely on remote assessments. Innovation in technology in the form of microelectromechanical systems (MEMs) has brought about great leaps forward in understanding of animal life. The MEMs are now readily available to ecologists for remotely monitoring the activities of wild animals. Since the advent of electronic tags, methods such as biologging are being increasingly applied to the study of animal ecology, providing information unattainable through other techniques. In this paper, we discuss a few relevant instances of biologging studies. We present an overview on biologging research area, describing the evolution of acquisition of behavioural information and the improvement provided by tags. In second part we will review some common data analysis techniques used to identify daily activity of animals

Novel tag-based method for measuring tailbeat frequency and variations in amplitude in fish

The tailbeat frequency (TBF) together with tailbeat amplitude (TBA) of fish are tightly correlated with swimming speed. In addition, these parameters can be used as indicators of metabolic rate and general activity level, provided that appropriate calibration studies have been performed in the laboratory. If an implantable bio-logger could measure TBF and TBA, it would, therefore, have great potential as a tool to monitor swimming behaviours and bioenergetics over extended periods of time in free roaming fish within natural or farm environments. The purpose of this study was, therefore, to establish a method for deriving accurate TBF and variations in TBA from activity tags that log high-resolution acceleration data. We used 6 tagged Atlantic salmon (Salmo salar) of ≈1 kg and subjected them to two types of swim trials in a large swim tunnel system. Test speeds were either incrementally increased in 20-min intervals until steady swimming ceased, or constant speed of 60 cm s−1 was given in a 4-h sustained test. The TBFs were visually observed by camera and compared with computed values from the activity tags. In the incremental trials the TBF increased linearly with swimming speed, while it remained constant during the 4 h of sustained swimming. The TBFs measured by activity tags were within ± 0.1 beat s−1 of the visual measurements across the swim speeds tested between 30 to 80 cm s−1. Furthermore, TBF and its corresponding relative swim speed were consistent between trial type. The relative TBA increased with swimming speed as a power function, showing that the fish relies on changes in both amplitude and frequency of tail movements when swimming at higher speeds, while adjustments of amplitude only play a minor part at lower speeds. These results demonstrate that TBFs can be measured accurately via activity tags, and thus be used to infer swimming activities and bioenergetics of free roaming fish. Furthermore, it is also possible to estimate changes in TBA via activity tags which allows for more nuanced assessments of swimming patterns in free roaming fish.publishedVersio

Alpine ungulate movement: Quantification of spatiotemporal environmental energetics and social interaction

Species movement, an animal’s ability to change its location, is a fundamental property of life, and animals have diverse physical and behavioural attributes that are believed to enhance efficient travel and optimization of resources. Quantifying movement energetics and returns to examine these ideas over relevant time- and space scales is, however, problematic. In this thesis, I set out to develop and use advanced biologging tag technology to determine a second by second account of the behaviour and location of tagged animals to unveil where and when key behaviours are occurring, to answer key questions about feeding and social behaviour, allocation in space and the energetic costs associated with different movement decisions. Specifically, I used accelerometers, magnetometers, temperature and pressure sensors with GPS units in animal-attached loggers to examine key questions linking movement, energetics and feeding and aggressive behaviours in 3 wild- and 3 domestic ungulate species in mountainous landscapes in the French Alps, monitored for periods between 30 and 200 days. To obtain high-frequency data using electronic devices for long periods, I had to first design new housings to attach safely the loggers to the animals and develop methods for weather proofing the loggers. I designed, using CAD-designa and 3D printing, different housing types and used ‘Guronic’ resin to shockproof and waterproof circuit boards. This allowed me to obtain logging data for up to 200 days. To give a location per second but stay within ethical weight restrictions, the dead-reckoning method to reconstruct fine-scale movements between low resolution GPS fixes was adopted. To improve the accuracy of dead-reckoning estimates I improved the method using behavioural definition to identify real moves (steps, grazing, moving) and distinguish it from resting, grooming and other behaviours not leading to a displacement of the animal in space, allowing to selectively filter data to be dead-reckon. Using the data collected, I showed that central-place-based, but free-roaming, domestic goats exhibited efficient space-use by having time-dependent fanning out from their central place, which reduced local resource depletion. Models predicted that area-use increased logarithmically with herd size and duration. These finding could lead to improved livestock management in multi-functional alpine landscapes, to reduce the risk of over-grazing and manage interactions with other grazing species and clonflicts with other landuse needs. The goat grazing patterns were compared to those of wild ibex and revealed goats to be more adaptable, with the ibex being particularly vulnerable to changes in temperature, exacerbated by them preferring steep slopes with associated high metabolic costs and heat generation during ascent. These results could further inform management decisions regarding the survival of alpine ibex under projected climate change. Furthermore I developed new biologging approaches to investigate social interactions, specifically head-clashing in both species. This agonistic behaviour was associated with competition and the rut in ibex and was quantified using methods first developed for the domestic goat, where the behaviour appeared to relate primarily to competition for food. Using the goat as a surrogate species, the behaviour could be identified and mapped for the ibex, which highlighted areas and times important for head-clashing, including drastic increases during the rut. Finally, movement data and proxies for energy expenditure from three domestic species (sheep, cows and goats) and three wild species (ibex, mouflon and chamois) was utilised to produce species-specific energy landscapes across the terrains they used. This indicated that different anatomies and behaviours resulted in different, species-specific, movement costs for specific topographies and habitats. Energy use for travel across heterogeneous space depends, therefore, on the species concerned. These findings thus highlight the importance to consider that species with different life histories and ecological needs use landscapes in contrasting ways and my results can provide a more refined evidence base for the management and conservation of these species in alpine grasslands. These biologging approaches allow now also to address further management issues such as the responses to disturbances from tourists (hiking, skiers, etc.) and even reveal how species are more susceptible to climate change

A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species

There are many advantages in determining animal behaviour for conservation initiatives seeking to protect species impacted by the changing planet. However, for many years, direct observation of elusive or dangerous animals in challenging habitats precluded the acquisition of representative, non-biased behavioural data. Recently though, animal-attached tag technology incorporating accelerometers, magnetometers and pressure sensors, has greatly advanced our abilities to document the behaviour of a numerous vertebrate species (e.g. birds, reptiles, fish, mammals), even when they cannot be observed. For this, supervised and unsupervised machine learning are often used to categorise behaviours by identifying patterns within the biotelemetry data. However, supervised machine learning requires training data, which is not always available, and both methods are driven by machine-based software with no explicitly defined parameters associated with behaviours which can be cross-checked. This work aimed to use a proper physics-based understanding of triaxial accelerometer-, magnetometer- and pressure data taken from electronic tags deployed on tiger sharks, Galeocerdo cuvier, to interpret patterns and group them into behaviours. As part of this, multi-modality in frequency distributions of parameters was investigated, on the premise that different behaviours can result in different frequency distributions in particular metrics. Following examination, algorithms using defined numerical limits were created to isolate distinct behaviours and these used to detect the extent of identified patterns within entire data sets and across individuals. A total of 12,338 minutes of tag data was processed, from which 10 behaviours were identified. Seven of these were successfully described using numerical metric limits from recorded and/or derived data including; ‘descent’, ‘ascent’, ‘burst power’, etc. However, frequency distributions showed a continuum rather than multiple distinct modes, indicating that this approach is likely to be more complex than thought. The use of physical principles seems a promising method for interpreting accelerometer, magnetometer and pressure data to identify behaviours that occur in study animals that cannot be directly observed. Although these algorithms are specific to tiger sharks in this work, this method is likely to be applicable to other species in aerial, aquatic or terrestrial habitats and could inform a broad range of conservations initiatives in the future

Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events

Animal Welfare Assessment

This Special Issue provides a collection of recent research and reviews that investigate many areas of welfare assessment, such as novel approaches and technologies used to evaluate the welfare of farmed, captive, or wild animals. Research in this Special Issue includes welfare assessment related to pilot whales, finishing pigs, commercial turkey flocks, and dairy goats; the use of sensors or wearable technologies, such as heart rate monitors to assess sleep in dairy cows, ear tag sensors, and machine learning to assess commercial pig behaviour; non-invasive measures, such as video monitoring of behaviour, computer vision to analyse video footage of red foxes, remote camera traps of free-roaming wild horses, infrared thermography of effort and sport recovery in sport horses; telomere length and regulatory genes as novel biomarkers of stress in broiler chickens; the effect of environment on growth physiology and behaviour of laboratory rare minnows and housing system on anxiety, stress, fear, and immune function of laying hens; and discussions of natural behaviour in farm animal welfare and maintaining health, welfare, and productivity of commercial pig herds

Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in ‘Daily Diary’ tags

Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals operate. However, interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. This work presents a single program, FRAMEWORK 4, that uses a particular sensor constellation described in the?Daily Diary? tag (recording tri-axial acceleration, tri-axial magnetic field intensity, pressure and e.g. temperature and light intensity) to determine the 4 key elements considered pivotal within the conception of the tag. These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal operates. The program takes the original data recorded by the Daily Dairy and transforms it into dead-reckoned movements,template-matched behaviours, dynamic body acceleration-derived energetics and positionlinked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.Fil: Walker, James S.. Swansea University. College Of Sciences; Reino UnidoFil: Jones, Mark W.. Swansea University. College Of Sciences; Reino UnidoFil: Laramee, Robert S.. Swansea University. College Of Sciences; Reino UnidoFil: Holton, Mark D.. Swansea University; Reino UnidoFil: Shepard, Emily L. C.. Swansea University. College Of Sciences; Reino UnidoFil: Williams, Hannah J.. Swansea University. College Of Sciences; Reino UnidoFil: Scantlebury, D. Michael. The Queens University Of Belfast; IrlandaFil: Marks, Nikki, J.. The Queens University Of Belfast; IrlandaFil: Magowan, Elizabeth A.. The Queens University Of Belfast; IrlandaFil: Maguire, Iain E.. The Queens University Of Belfast; IrlandaFil: Grundy, Ed. Swansea University. College Of Sciences; Reino UnidoFil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue; ArgentinaFil: Wilson, Rory P.. Swansea University. College Of Sciences; Reino Unid