[MARMAM] New publication: HMMs for animal accelerometer data

Theoni Photopoulou theoni.photopoulou at gmail.com
Mon Oct 31 06:51:49 PDT 2016

Dear all,

I am pleased to announce the publication of our paper in Methods in Ecology
and Evolution. Though the case studies do not
 marine mammals, I hope the method will be of general interest.

Vianey Leos-Barajas,
​ ​
Theoni Photopoulou,
​ ​
land Langrock,
​ ​
Toby A. Patterson,
​ ​
Yuuki Y. Watanabe,
​ ​
​ ​
Yannis P. Papastamatiou
​ ​
(2016). Analysis of animal accelerometer data using hidden
​ ​
Markov models. Methods in Ecology and Evolution.
​ ​
DOI: 10.1111/2041-210X.12657

​1. ​
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.
​2. ​
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.

​3. ​
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.
​4. 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.

​You can access the article online
<http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12657/full> or from
me (theoni.photopoulou at gmail.com).


Theoni Photopoulou, Postdoctoral Fellow
Institute for Coastal and Marine Research, Nelson Mandela Metropolitan
University, South Africa
Centre for Statistics in Ecology Environment and Conservation, University
of Cape Town, South Africa
" Be silly. Be honest. Be kind " Ralph Waldo Emerson
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.uvic.ca/pipermail/marmam/attachments/20161031/2659a5f0/attachment.html>

More information about the MARMAM mailing list