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dc.contributor.advisorBuckland, S. T. (Stephen T.)
dc.contributor.advisorThomas, Len
dc.contributor.advisorNewman, Ken B.
dc.contributor.authorBishop, Jonathan R. B.
dc.coverage.spatial403en
dc.date.accessioned2009-06-25T14:10:19Z
dc.date.available2009-06-25T14:10:19Z
dc.date.issued2009-06-24
dc.identifier.urihttp://hdl.handle.net/10023/718
dc.description.abstractMark-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.en
dc.format.extent1341692 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of St Andrews
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectPopulation dynamics modelsen
dc.subjectState-space modelsen
dc.subjectSequential importance samplingen
dc.subjectParticle filteren
dc.subjectConditional generationen
dc.subjectMark-recaptureen
dc.subjectSoay sheepen
dc.titleEmbedding population dynamics in mark-recapture modelsen
dc.typeThesisen
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dc.publisher.institutionThe University of St Andrewsen


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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted within the work, this item's license for re-use is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported