Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

The recent increase in data accuracy from high resolution accelerometers
offers substantial potential for improved understanding and prediction of
animal movements. However, current approaches used for analysing these
multivariable datasets typically require existing knowledge of the
behaviors of the animals to inform the behavioral classification process.
These methods are thus not well-suited for the many cases where limited
knowledge of the different behaviors performed exist. Here, we introduce
the use of an unsupervised learning algorithm. To illustrate the
method's capability we analyse data collected using a combination of
GPS and Accelerometers on two seabird species: razorbills (Alca torda) and
common guillemots (Uria aalge). We applied the unsupervised learning
algorithm Expectation Maximization to characterize latent behavioral
states both above and below water at both individual and group level. The
application of this flexible approach yielded significant new insights
into the foraging strategies of the two study species, both above and
below the surface of the water. In addition to general behavioral modes
such as flying, floating, as well as descending and ascending phases
within the water column, this approach allowed an exploration of
previously unstudied and important behaviors such as searching and prey
chasing/capture events. We propose that this unsupervised learning
approach provides an ideal tool for the systematic analysis of such
complex multivariable movement data that are increasingly being obtained
with accelerometer tags across species. In particular, we recommend its
application in cases where we have limited current knowledge of the
behaviors performed and existing supervised learning approaches may have
limited utility.