Data from: Analysis of animal accelerometer data using hidden Markov models

Use of accelerometers is now widespread within animal biologging 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
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, an HMM can be used for state prediction or to make
inferences about drivers of behaviour. For state prediction, a supervised
learning approach can be applied. That is, an HMM is trained to classify
unlabelled acceleration data into a finite set of pre-specified
categories. An unsupervised learning approach can be used to infer new
aspects of animal behaviour when biologically meaningful response
variables are used, with the caveat that the states may not map to
specific behaviours. We provide the details necessary to implement and
assess an HMM in both the supervised and unsupervised learning context and
discuss the data requirements of each case. We outline two applications to
marine and aerial systems (shark and eagle) taking the unsupervised
learning 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 and
can easily be extended to suit a wide range of types of animal activity
data. The ability to combine direct observations of animal activity with
statistical models, which account for the features of accelerometer data,
offers a new way to quantify animal behaviour and energetic expenditure
and to deepen our insights into individual behaviour as a constituent of
populations and ecosystems.