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dc.contributor.advisorKing, Ruth
dc.contributor.advisorHarwood, John
dc.contributor.advisorFrederiksen, Morten
dc.contributor.advisorHarris, Michael P.
dc.contributor.advisorWanless, S. (Sarah)
dc.contributor.authorReynolds, Toby J.
dc.coverage.spatial180 p.en_US
dc.description.abstractIntegrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework. This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period. Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.en_US
dc.publisherUniversity of St Andrews
dc.relationReynolds, T. J., King, R., Harwood, J., Frederiksen, M., Harris, M. P. & Wanless, S. (2009). Integrated data analysis in the presence of emigration and mark loss. Journal of Agricultural, Biological and Environmental Statistics 14, 411-431.en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectBayesian inferenceen_US
dc.subjectCommon guillemot (Uria aalge)en_US
dc.subjectIntegrated population modelen_US
dc.subjectMarkov chain Monte Carlo (MCMC)en_US
dc.subjectMulti-population dynamicsen_US
dc.subjectSpatiotemporal variabilityen_US
dc.subject.lcshAnimal populations--Mathematical modelsen_US
dc.subject.lcshMarkov processesen_US
dc.subject.lcshMonte Carlo methoden_US
dc.subject.lcshBayesian statistical decision theoryen_US
dc.subject.lcshCommon murre--Scotland--May, Isle ofen_US
dc.titleBayesian modelling of integrated data and its application to seabird populationsen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.publisher.departmentCentre for Ecology and Hydrologyen_US

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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