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Use of accelerometers is now widespread within animal biotelemetry as theyprovide a means of measuring an animal's activity in a meaningful andquantitative way where direct observation is not possible. In sequentialacceleration data there is a natural dependence between observations ofmovement or behaviour, a fact that has been largely ignored in most analyses.Analyses of acceleration data where serial dependence has been explicitlymodelled have largely relied on hidden Markov models (HMMs). Depending on theaim of an analysis, either a supervised or an unsupervised learning approachcan be applied. Under a supervised context, an HMM is trained to classifyunlabelled acceleration data into a finite set of pre-specified categories,whereas we will demonstrate how an unsupervised learning approach can be usedto infer new aspects of animal behaviour. We will provide the details necessaryto implement and assess an HMM in both the supervised and unsupervised context,and discuss the data requirements of each case. We outline two applications tomarine and aerial systems (sharks and eagles) taking the unsupervised approach,which is more readily applicable to animal activity measured in the field. HMMswere used to infer the effects of temporal, atmospheric and tidal inputs onanimal behaviour. Animal accelerometer data allow ecologists to identifyimportant correlates and drivers of animal activity (and hence behaviour). TheHMM framework is well suited to deal with the main features commonly observedin accelerometer data. The ability to combine direct observations of animalsactivity and combine it with statistical models which account for the featuresof accelerometer data offer a new way to quantify animal behaviour, energeticexpenditure and deepen our insights into individual behaviour as a constituentof populations and ecosystems.

Analysis of animal accelerometer data using hidden Markov models