Library Open Repository

Statistical inference for movement behaviour using animal tracking data


Downloads per month over past year

Thomson, RB (2008) Statistical inference for movement behaviour using animal tracking data. PhD thesis, University of Tasmania.

PDF (Whole thesis)
whole_ThomsonRo...pdf | Download (14MB)
Available under University of Tasmania Standard License.

| Preview


Satellite tracking provides an opportunity to learn about how animals choose to
move and about the covariates of movement. Quantitative methodology for this
problem has lagged behind the remote sensing technology that provides both animal
tracks and covariate information. A statistical framework capable of providing
appropriate hypothesis testing has to couple very different types of data: highly autocorrelated
time series of observed locations (the track), with 2-dimensional maps of
" covariate data. In addition, animals respond to internal motivations, representable
only as theorised motivations. Behaviour is likely to be highly complex and to be
only approximately understood so that process error cannot be ignored. Observation
error should be accounted for separately from process error because longitude
is typically more difficult to estimate than latitude, and because estimates of observation
error are sometimes available. State space models account separately for
observation and process errors, and model the serial correlation inherent in tracks.
State space models offer great flexibility, nevertheless, the means of incorporating
diverse movement behaviours and covariate information is not immediately clear. ,
Traditionally, these models require that time series be equally spaced in time (seldom
the case with observed tracks) and they have presented substantial difficulties
in inference.
This thesis presents a flexible Bayesian state space modelling framework suitable
for application to tracks. Movement behaviour is incorporated through advection
fields that represent movement hypotheses. These are calculated using theories regarding
movement behaviour, possibly coupled with covariate information. The deviance
information criterion DIC measures the weight given by observed track data
to alternative proposed hypotheses regarding movement behaviour. In simulation,
DIC successfully discriminated the advection fields, and therefore the movement
hypotheses, used to simulate track data. DIC is less sensitive than the Bayes factor
is, to the priors, an advantage in a fielq in which little prior information is available.
Markov chain Monte Carlo sampling successfully facilitated nonlinear, non-Gaussian
model forms while avoiding the inference problems encountered by practitioners of
sequential importance sampling. Latent locations are estimated, allowing realistic,
nonlinear path estimation. Inertia, a tendency for directional persistence, is incorporated.
The Bayesian approach allows the incorporation of prior information and
eases inference. Temporal shifts in behaviour are also modelled. The method is
demonstrated in practice, using satellite tracks from white sharks in Australia. The
problem of modelled animals becoming trapped in semi-enclosed areas and stepping
across narrow barriers is discussed and a proposed solution, using Laplace's equation
to provide advective flow around obstacles, is demonstrated.

Item Type: Thesis (PhD)
Keywords: Home range (Animal geography), Sharks
Copyright Holders: The Author
Copyright Information:

Copyright 2008 the author

Additional Information:

Available for library use only and copying in accordance with the Copyright Act 1968, as amended. Thesis (PhD)--University of Tasmania, 2008. Includes bibliographical references. 1. General introduction -- 2. Literature review -- 3. Framework for animal movement behaviour -- 4. Changing behaviour through time -- 5. Bayesian model comparison -- 6. Harmonic mean estimator inappropriate -- 7. Using Laplace's equation to move around obstacles -- 8. Application to white shark tracks -- 9. General conclusion -- 10. Symbols used in this thesis -- 11. Posterior and conditionals -- 12. Gamma conditional distribution -- 13. Multivariate normal conditional distribution -- 14. Wishart conditional distribution -- 15. R code for figure

Date Deposited: 04 Feb 2015 23:34
Last Modified: 05 Apr 2016 03:52
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page