Developing an automated cognitive bias task for wild squirrels
Grantees: Vikki Neville and Lisa Leaver
Institutions: University of Bristol, University of Exeter, United Kingdom
Grant amount: $157,049
Grant type: Challenge grant
Focal species: Grey squirrel (Sciurus carolinensis)
Conservation status: Least concern
Disciplines: Animal welfare science, physiology, animal behavior
Research location: United Kingdom
Project summary
The development of the cognitive judgment bias (CJB) task for non-human animals revolutionised the field of animal welfare. The CJB task: (a) is thought to measure both relatively better and relatively poorer welfare across the full spectrum of possible welfare states, (b) is non-invasive, and (c) has been validated using a meta-analytic approach. Moreover, the theoretical basis of the predictions for the CJB task should be applicable across taxa and, accordingly, it has been used successfully in many species. However, to date, its use has largely been in captive species and there are a dearth of examples in wild animals. A key barrier to implementation is that it is difficult to train animals to associate stimuli with particular outcomes: the time needed to do so makes these studies unsuccessful or infeasible in non-captive populations of animals. We propose that this could be overcome by capitalising on the natural behaviour of a species to reduce training time and by using equipment that allows automation of stimulus presentation and data collection.
The main aim of our proposed project is to develop a CJB task for use in wild animals, in particular wild squirrels. Our key objectives are to develop a task for collecting CJB data from grey squirrels which: (1) capitalises on their innate behaviour, obviating the need for extensive training, and (2) makes use of Raspberry Pi equipment so that the task can be easily implemented by other researchers and straightforwardly adapted for use across species to measure welfare and validate novel potential measures of welfare.
To assess that our task works as anticipated, secondary objectives will be to: (1) assess how CJB varies with task manipulations designed to alter affective valence (the distance of the equipment from cover, and levels of food provisioning in the environment), and (2) assess how CJB correlates with other potential non-invasive indicators of welfare (e.g. flight distance in response to humans, QBA scores, retrapability, social status, hair cortisol concentration, and fluctuations in eye temperature following positive and negative stimuli on the judgement bias test measured using infrared thermal cameras).
Why we funded this project
As noted above, cognitive bias tests are generally considered to be exceptionally robust welfare indicators, while gray squirrels are an abundant species and a prime target for near-term interventions. Therefore, we are excited to support the development of a cognitive judgment bias test for use on free-living gray squirrels, which will also help to assess the validity of simpler indicators, such as eye temperature and flight initiation distance.