Data from: 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-second 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.