title: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data.
abstract: 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.
authors: School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK; Marine Scotland Science Scottish Government Marine Laboratory PO Box 101375 Victoria Road Aberdeen AB11 9DB UK.
authors: Marianna
authors: M
authors: Chimienti
authors: School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK.
authors: Thomas
authors: T
authors: Cornulier
authors: RSPB Centre for Conservation Science North Scotland Office Etive House, Beechwood Park Inverness IV2 6AL UK.
authors: Ellie
authors: E
authors: Owen
authors: RSPB Centre for Conservation Science The Lodge Sandy Bedfordshire SG19 2DL UK.
authors: Mark
authors: M
authors: Bolton
authors: Marine Scotland Science Scottish Government Marine Laboratory PO Box 101 375 Victoria Road Aberdeen AB11 9DB UK.
authors: Ian M
authors: IM
authors: Davies
authors: School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK.
authors: Justin M J
authors: JM
authors: Travis
authors: School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK.
authors: Beth E
authors: BE
authors: Scott
keywords: Accelerometer data; animal movements; behavioral classification; unsupervised learning