Research@StAndrews
 
The University of St Andrews

Research@StAndrews:FullText >
Mathematics & Statistics (School of) >
Statistics >
Statistics Theses >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/718
This item has been viewed 32 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
Jonathan R. B. Bishop PhD thesis.PDF1.31 MBAdobe PDFView/Open
Title: Embedding population dynamics in mark-recapture models
Authors: Bishop, Jonathan R. B.
Supervisors: Buckland, Stephen T.
Thomas, Len
Newman, Ken B.
Keywords: Population dynamics models
State-space models
Sequential importance sampling
Particle filter
Conditional generation
Mark-recapture
Soay sheep
Issue Date: 24-Jun-2009
Abstract: Mark-recapture methods use repeated captures of individually identifiable animals to provide estimates of properties of populations. Different models allow estimates to be obtained for population size and rates of processes governing population dynamics. State-space models consist of two linked processes evolving simultaneously over time. The state process models the evolution of the true, but unknown, states of the population. The observation process relates observations on the population to these true states. Mark-recapture models specified within a state-space framework allow population dynamics models to be embedded in inference ensuring that estimated changes in the population are consistent with assumptions regarding the biology of the modelled population. This overcomes a limitation of current mark-recapture methods. Two alternative approaches are considered. The "conditional" approach conditions on known numbers of animals possessing capture history patterns including capture in the current time period. An animal's capture history determines its state; consequently, capture parameters appear in the state process rather than the observation process. There is no observation error in the model. Uncertainty occurs only through the numbers of animals not captured in the current time period. An "unconditional" approach is considered in which the capture histories are regarded as observations. Consequently, capture histories do not influence an animal's state and capture probability parameters appear in the observation process. Capture histories are considered a random realization of the stochastic observation process. This is more consistent with traditional mark-recapture methods. Development and implementation of particle filtering techniques for fitting these models under each approach are discussed. Simulation studies show reasonable performance for the unconditional approach and highlight problems with the conditional approach. Strengths and limitations of each approach are outlined, with reference to Soay sheep data analysis, and suggestions are presented for future analyses.
URI: http://hdl.handle.net/10023/718
Type: Thesis
Publisher: University of St Andrews
Appears in Collections:Statistics Theses



This item is protected by original copyright

This item is licensed under a Creative Commons License
Creative Commons

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

DSpace Software Copyright © 2002-2012  Duraspace - Feedback
For help contact: Digital-Repository@st-andrews.ac.uk | Copyright for this page belongs to St Andrews University Library | Terms and Conditions (Cookies)