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

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

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Horses and Risk

The risk of physical accident or injury to humans from horses is well understood. Now, there is increasing awareness of negative impacts on the physical and mental wellbeing of horses from living in fundamentally human environments and being subject to human management regimes. The 17 articles in this collection describe horse-related risks to humans and human-related risks to horses across a range of equestrian disciplines, sectors and activities. Not only do the authors take detailed approaches towards describing and calculating risk, they suggest solutions-focussed interventions for reducing the consequence and likelihood of harm to horses and humans in their relations with one another. Together, these important articles provide strategies for maximising the mutual benefits of humans and horses in each other’s lives. By considering human, animal and environmental aspects of horse-related risk, this collection provides a foundation for the development of an ‘Equestrian One Health’ framework to underpin future research into horse-related risks

Cat-People: An Ethnography of More-Than-Human Interrelatedness in the Cat Fancy

The practice of breeding and showing pedigree cats, termed the ‘cat fancy’, provides a novel lens through which to explore more-than-human intersections within leisure. Based on multispecies ethnographic fieldwork in the United Kingdom at cat shows and drawing on interviews with those who breed and exhibit cats, as well as judges and veterinarians, the thesis considers the relationships and sociality between humans and cats that form within the fancy. Going beyond a typically anthropocentric approach to leisure, it engages with feline subjectivities and asks, ‘what’s in it for the cats?’. This question is not one that seems to arise often in the consciousness of breeders or exhibitors. The cats themselves may benefit from specific standards of care, including health provisions and general daily needs. Yet, the thesis contends that the cat fancy involves serious compromises to the well-being and agency of the cat. The selective breeding of human-constructed cat breeds and the establishment of the cat fancy itself has restricted or removed feline agency. The processes and discourses disseminated and controlled by cat fancy institutions also represent an exercise of biopower, the overall aim being the ‘improvement’ of breeds and the preservation of ‘lineage’ and ‘pedigree’. The evaluative logic used within reproductive decision-making shares characteristics with eugenicism. The thesis does not deny that humans and cats form close intersubjective bonds in the cat fancy, indeed, such bonds are clearly in evidence. At the same time, however, multifarious, coinciding and conflicting relations and conceptualisations of cats emerge. Cats may simultaneously act as kin, companions, social conduits, status symbols, extensions of self, collaborators in cat fancy success or failure, lively commodities, and objects for aesthetic evaluation. The cat fancy also produces humans who self-define as ‘cat people’ and ‘ethical breeders’ with shared norms of care and attitudes towards cats. Overall, despite allowing the production of heterogeneous human-cat relations, the thesis argues that prevailing discourses, practices, and norms of care in the cat fancy result in the prioritisation of human needs

Horsing Around—A Dataset Comprising Horse Movement

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

Horsing Around: A Dataset Comprising Horse Movement

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