Quantifying behavior and life‐history events of an Arctic ungulate from year‐long continuous accelerometer data

Remote Monitoring of Canine Patients Treated for Pruritus during the COVID-19 Pandemic in Florida Using a 3-D Accelerometer

The medical management of chronic canine pruritic dermatologic conditions is challenging and often frustrating. This is a report that shows one way of aiding the management of pruritic dogs using a remote monitoring device. It is often difficult for veterinarians to get dog owners to return to the clinic once a dog is treated. It is possible that a 3-D accelerometer device could provide information to the clinic staff on the success or failure of a pruritus treatment plan while the dog was cared for at home. Eighty-seven dogs and their owners came to a Florida dermatology specialty clinic or its general practice hospital to be evaluated and treated for pruritus. An ANIMO® 3-D accelerometer was placed on the collar of dogs diagnosed and treated for pruritus. Dogs that completed this study were monitored for 120 days (4 months). The ANIMO smart phone application monitored a dog's daily scratching, shaking, sleeping, activity, and resting and summarized this information in a daily report visible on the pet owner's smart phone. An additional variable (grooming minutes per day) could be seen by the study team that was not yet available in the app. The use of a 3-D accelerometer enabled veterinarians to continuously monitor dogs at home when they were being treated for itching. Clinic staff kept in touch with the owners by phone and could change therapy or bring the dog back for a recheck if problems were seen. Daily reports were combined into line charts that showed plots of scratching, shaking, grooming, and sleeping over four months. Veterinarians were able to remotely monitor dogs that had been treated for pruritus for up to four months through use of a collar-borne monitoring device. Dog owners and clinic staff used the daily summaries accessible through a smart phone application. Dogs seemed to tolerate the device well because of its small size, light weight, long battery life, and unobtrusive nature.

Prospecting movements link phenotypic traits to female annual potential fitness in a nocturnal predator

Recent biologging technology reveals hidden life and breeding strategies of nocturnal animals. Combining animal movement patterns with individual characteristics and landscape features can uncover meaningful behaviours that directly influence fitness. Consequently, defining the proximate mechanisms and adaptive value of the identified behaviours is of paramount importance. Breeding female barn owls (Tyto alba), a colour-polymorphic species, recurrently visit other nest boxes at night. We described and quantified this behaviour for the first time, linking it with possible drivers, and individual fitness. We GPS-equipped 178 female barn owls and 122 male partners from 2016 to 2020 in western Switzerland during the chick rearing phase. We observed that 111 (65%) of the tracked breeding females were (re)visiting nest boxes while still carrying out their first brood. We modelled their prospecting parameters as a function of brood-, individual- and partner-related variables and found that female feather eumelanism predicted the emergence of prospecting behaviour (less melanic females are usually prospecting). More importantly we found that increasing male parental investment (e.g., feeding rate) increased female prospecting efforts. Ultimately, females would (re)visit a nest more often if they had used it in the past and were more likely to lay a second clutch afterwards, consequently having higher annual fecundity than non-prospecting females. Despite these apparent immediate benefits, they did not fledge more chicks. Through biologging and long-term field monitoring, we highlight how phenotypic traits (melanism and parental investment) can be related to movement patterns and the annual potential reproductive output (fecundity) of female barn owls.

Applications of Accelerometers and Other Bio-Logging Devices in Captive and Wild Animals

Bio-logging devices have been widely used in ecology across a range of species to acquire information on the secret lives of animals in the wild, which would otherwise be challenging to obtain via direct observations [...].

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

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