Movement data from marine animals tagged with electronic tags are becoming
increasingly diverse and plentiful. This trend entails a need for statistical methods
that are able to filter the observations to extract the ecologically relevant
content. This dissertation focuses on the development and application of hidden
Markov models (HMMs) for analysis of movement data from sh. The main
contributions are represented by six scientific publications.
Estimation of animal location from uncertain and possibly indirect observations
is the starting point of most movement data analyses. In this work a discrete
state HMM is employed to deal with this task. Specifically, the continuous
horizontal plane is discretised into grid cells, which enables a state-space model
for the geographical location to be estimated on this grid.
The estimation model for location is extended with an additional state representing
the behaviour of the animal. With the extended model can migratory
and resident movement behaviour be related to geographical regions. For population
inference multiple individual state-space analyses can be interconnected
using mixed effects modelling. This framework provides parameter estimates at
the population level and allows ecologists to identify individuals that deviate
from the rest of the tagged population.
The thesis also deals with geolocation on state-spaces with complicated geometries.
Using an unstructured discretisation and the finite element method tortuous
shore line geometries are closely approximated. This furthermore enables
accurate probability densities of location to be computed.
Finally, the performance of the HMM approach in analysing nonlinear state
space models is compared with two alternatives: the AD Model Builder framework
and BUGS, which relies on Markov chain Monte Carlo estimation.