Dataset Information

Using the incidence and impact of behavioural conditions in guide dogs to investigate patterns in undesirable behaviour in dogs.

ABSTRACT: The domestic dog is one of our most popular companions and longest relationships, occupying different roles, from pet to working guide dog for the blind. As dogs age different behavioural issues occur and in some cases dogs may be relinquished or removed from their working service. Here we analyse a dataset on working guide dogs that were removed from their service between 1994 and 2013. We use the withdrawal reasons as a proxy for the manifestation of undesirable behaviour. More than 7,500 dogs were in the dataset used, 83% of which were retired (due to old age) and 17% were withdrawn for behavioural issues. We found that the main reasons for behaviour withdrawal were environmental anxiety, training, and fear/aggression. Breed and sex had an effect on the odds of dogs being withdrawn under the different reasons. The age at withdrawal for the different withdrawal reasons suggested that dogs were more likely to develop fear/aggression related issues early on, whilst issues related to training could develop at almost any age. We found no evidence for heterosis effecting behaviour. We believe that this work is relevant to the pet dog population and had implications for understanding ageing and genetic influences on behaviour.

SUBMITTER: Caron-Lormier G

PROVIDER: S-EPMC4831008 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Using the incidence and impact of behavioural conditions in guide dogs to investigate patterns in undesirable behaviour in dogs.

Caron-Lormier Geoffrey G Harvey Naomi D ND England Gary C W GC Asher Lucy L

Scientific reports 20160414

The domestic dog is one of our most popular companions and longest relationships, occupying different roles, from pet to working guide dog for the blind. As dogs age different behavioural issues occur and in some cases dogs may be relinquished or removed from their working service. Here we analyse a dataset on working guide dogs that were removed from their service between 1994 and 2013. We use the withdrawal reasons as a proxy for the manifestation of undesirable behaviour. More than 7,500 dogs ...[more]

PMID: 27075868

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Project description:BackgroundImplementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations.MethodsResearchers from Canada, the UK and Australia attended a 3-day meeting in December 2012 to build an international collaboration among researchers and decision-makers interested in the advancing use of the TDF. The participants were experienced in using the TDF to assess implementation problems, design interventions, and/or understand change processes. This guide is an output of the meeting and also draws on the authors' collective experience. Examples from the implementation research literature judged by authors to be representative of specific applications of the TDF are included in this guide.ResultsWe explain and illustrate methods, with a focus on qualitative approaches, for selecting and specifying target behaviours key to implementation, selecting the study design, deciding the sampling strategy, developing study materials, collecting and analysing data, and reporting findings of TDF-based studies. Areas for development include methods for triangulating data, e.g. from interviews, questionnaires and observation and methods for designing interventions based on TDF-based problem analysis.ConclusionsWe offer this guide to the implementation community to assist in the application of the TDF to achieve implementation objectives. Benefits of using the TDF include the provision of a theoretical basis for implementation studies, good coverage of potential reasons for slow diffusion of evidence into practice and a method for progressing from theory-based investigation to intervention.

Project description:Background:Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. Methods:We address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. Results:The overall recognition accuracy of >?94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. Conclusions:Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

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Dataset Information

Using the incidence and impact of behavioural conditions in guide dogs to investigate patterns in undesirable behaviour in dogs.

Publications

Using the incidence and impact of behavioural conditions in guide dogs to investigate patterns in undesirable behaviour in dogs.

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