Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild

Abstract

Tri-axial accelerometer tags provide quantitative data on body movement that can be used to characterize behaviour and understand species ecology in ways that would otherwise be impossible. Using tags on wild terrestrial mammals, especially smaller species, in natural settings has been limited. Poor battery power also reduced the amount of data collected, which limits what can be derived about animal behaviour. Another challenge using wild animals, is acquiring observations of actual behaviours with which to compare tag data and create an adequate training set to reliably identify behavioural states. Brown hares were fitted with accelerometers for 5 weeks to evaluate their use in collecting detailed behaviour data and activity levels. Collared hares were filmed to associate actual behaviours with tag data. Observed behaviours were classified using Random Forests (ensemble learning method) to create a supervised model and then used to classify hare behaviour from the tags. Increased tag longevity allowed acquisition of large quantities of data from each individual and direct observation of tagged hare's behaviour. Random Forests accurately classified observed behaviours from tag data with an 11% error rate. Individual accuracy of behaviours varied with running (100% accuracy), feeding (94.7%) and vigilance (98.3%) having the highest classification accuracy. Hares spent 46% of their time being vigilant and 25% feeding when active. The combination of our tags and Random Forests facilitated large amounts of behavioural data to be collected on animals where observational studies could be limited, or impossible. The same method could be used on a range of terrestrial mammals to create models to investigate behaviour from tag data, to learn more about their behaviour and be used to answer many ecological questions. However, further development of methods for analysing tag data is needed to make the process quicker, simpler and more accurate.