Hidden Markov Models for Animal Movement and other Ecological Data Workshop

Mary Woodcock Kroble
Tuesday 4 April 2017
Start date: 10 August 2017 - End date: 11 August 2017
Time: 9:00 am - 5:00 pm

This 2-day workshop gives a comprehensive introduction to the application of HMMs to ecological data, comprising both theoretical lectures and hands-on practical sessions using R. In particular, participants will have the chance to analyse their own data within the practical sessions, supported by statisticians with considerable expertise on HMMs.


Hidden Markov models (HMMs) are flexible statistical models for sequences of observations that are driven by underlying states. In recent years, HMMs have become increasingly popular within the ecological community as versatile general-purpose tools for the analysis of animal behaviour data collected over time, and particularly animal movement data. HMMs are good models for many ecological data primarily because they often constitute a very natural and intuitive approach, especially when the states can be interpreted as corresponding roughly to biologically meaningful entities, e.g. behavioural states or survival states. In such cases the HMM can usually be regarded as a good representation of the biological reality in terms of a fairly simple mathematical model. This then opens up the way for various types of biologically interesting inference to be drawn, including e.g. the effect of environmental conditions on animal behaviour. Secondly, despite their relatively complex structure, HMMs turn out to be surprisingly easy to handle and are computationally feasible in almost all of the examples ecologists are dealing with on a regular basis.


This workshop is aimed at ecologists working with time series data on animal movement and general animal behaviour. It will be expected that participants are familiar with very basic statistics and probability theory (e.g. what is a probability distribution? what is a random variable? what is conditional probability?). Basic knowledge of the free software R would be advantageous, but is not required.


In the theoretical sessions, the following topics will be covered:

  • overview & basic model formulation
  • fitting an HMM to data
  • model selection & model checking
  • state decoding
  • incorporating covariates, random effects and seasonality
  • extensions of the basic model formulation (e.g. multivariate time series)

The theoretical sessions will be accompanied by practical sessions using the statistical computing software R, where the theoretical concepts are implemented and illustrated using a real-data case study provided by us (or, optionally, the participants’ own data). The practical sessions will largely be based on the R package moveHMM, though we may occasionally also write code from scratch, depending on the participants’ interests.


The theoretical workshop will be led by a team including Roland Langrock, Jennifer Pohle, Timo Adam (Bielefeld University), Théo Michelot (University of Sheffield), and Stacy DeRuiter (Calvin College).

Logistics & Computing Facilities

The workshop will take place at the Centre for Ecological and Environmental Modelling (CREEM), University of St Andrews, Scotland, from 10-11 August 2017. Workshop participants are encouraged to bring their own laptops if possible, although lab computers will be available if needed.


Payment is made via the University of St Andrews online payment service. You will be asked to register if it is your first visit to the store. Please follow this link to the Online Payment Service.  Confirmation of a place on the workshop will follow once payment has been received.


Participants are responsible for organising and paying for their own room and board (except for lunches and coffees, which will be provided during the workshop). Further information about accommodation in St Andrews is available online. University accommodation may be a convenient choice, with some options in very close walking distance to CREEM.

Location and Travel Information

Follow this link for Location and Travel Information

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