  Use of accelerometers is now widespread within animal biotelemetry as they
provide a means of measuring an animal's activity in a meaningful and
quantitative way where direct observation is not possible. In sequential
acceleration data there is a natural dependence between observations of
movement or behaviour, a fact that has been largely ignored in most analyses.
Analyses of acceleration data where serial dependence has been explicitly
modelled have largely relied on hidden Markov models (HMMs). Depending on the
aim of an analysis, either a supervised or an unsupervised learning approach
can be applied. Under a supervised context, an HMM is trained to classify
unlabelled acceleration data into a finite set of pre-specified categories,
whereas we will demonstrate how an unsupervised learning approach can be used
to infer new aspects of animal behaviour. We will provide the details necessary
to implement and assess an HMM in both the supervised and unsupervised context,
and discuss the data requirements of each case. We outline two applications to
marine and aerial systems (sharks and eagles) taking the unsupervised approach,
which is more readily applicable to animal activity measured in the field. HMMs
were used to infer the effects of temporal, atmospheric and tidal inputs on
animal behaviour. Animal accelerometer data allow ecologists to identify
important correlates and drivers of animal activity (and hence behaviour). The
HMM framework is well suited to deal with the main features commonly observed
in accelerometer data. The ability to combine direct observations of animals
activity and combine it with statistical models which account for the features
of accelerometer data offer a new way to quantify animal behaviour, energetic
expenditure and deepen our insights into individual behaviour as a constituent
of populations and ecosystems.
