The statistical development of integrated multi-state stopover models
Abstract
This thesis focusses on the analysis of ecological capture-recapture data and the
estimation of population parameters of interest. Many of the common models applied
to such data, for example the Cormack-Jolly-Seber model, condition on the first capture of an individual or on the number of individuals encountered. A consequence
of this conditioning is that it is not possible to estimate the total abundance
directly. Stopover models remove the conditioning on first capture and instead
explicitly model the arrival of individuals into the population. This permits the
estimation of abundance through the likelihood along with other parameters such
as capture and retention probabilities.
We develop an integrated stopover model capable of analysing multiple years of
data within a single likelihood and allowing parameters to be shared across years.
We consider special cases of this model, writing the likelihood using sufficient statistics
as well as utilising the hidden Markov model framework to allow for efficient
evaluation of the likelihood. We further extend this model to an integrated multistate-stopover model which incorporates any available discrete state information.
The new stopover models are applied to real ecological data sets. A cohort-dependent
single-year stopover model is applied to data on grey seals, Halichoerus
grypus, where the cohorts are determined by birth year. The integrated stopover
model and integrated multi-state stopover model are used to analyse a data set on
great crested newts, Triturus cristatus. A subset of this data is used to explore closed population models that permit capture probabilities to depend on discrete
state information. The final section of this thesis considers a capture-recapture-recovery
data set relating to Soay sheep, a breed of domestic sheep Ovis aries.
These data contain individual time-varying continuous covariates and raise the issue
of dealing with missing data.
Type
Thesis, PhD Doctor of Philosophy
Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.