Bayesian modelling of integrated data and its application to seabird populations
Integrated 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 guillemo...
Main Author: | |
---|---|
Other Authors: | , , , , |
Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
Published: |
University of St Andrews
2010
|
Subjects: | |
Online Access: | http://hdl.handle.net/10023/1635 |
id |
ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/1635 |
---|---|
record_format |
openpolar |
spelling |
ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/1635 2023-07-02T03:32:01+02:00 Bayesian modelling of integrated data and its application to seabird populations Reynolds, Toby J. King, Ruth Harwood, John Frederiksen, Morten Harris, Michael P. Wanless, S. (Sarah) 180 p. 2010-12-13T16:23:13Z application/pdf http://hdl.handle.net/10023/1635 en eng University of St Andrews The University of St Andrews Centre for Ecology and Hydrology Reynolds, 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. http://hdl.handle.net/10023/1635 Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported http://creativecommons.org/licenses/by-nc-nd/3.0/ Bayesian inference Common guillemot (Uria aalge) Integrated population model Markov chain Monte Carlo (MCMC) Multi-population dynamics Projections Spatiotemporal variability QL752.R48 Animal populations--Mathematical models Markov processes Monte Carlo method Bayesian statistical decision theory Common murre--Scotland--May Isle of Thesis Doctoral PhD Doctor of Philosophy 2010 ftstandrewserep 2023-06-13T18:30:58Z Integrated 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 ... Doctoral or Postdoctoral Thesis common guillemot Common Murre Uria aalge uria University of St Andrews: Digital Research Repository |
institution |
Open Polar |
collection |
University of St Andrews: Digital Research Repository |
op_collection_id |
ftstandrewserep |
language |
English |
topic |
Bayesian inference Common guillemot (Uria aalge) Integrated population model Markov chain Monte Carlo (MCMC) Multi-population dynamics Projections Spatiotemporal variability QL752.R48 Animal populations--Mathematical models Markov processes Monte Carlo method Bayesian statistical decision theory Common murre--Scotland--May Isle of |
spellingShingle |
Bayesian inference Common guillemot (Uria aalge) Integrated population model Markov chain Monte Carlo (MCMC) Multi-population dynamics Projections Spatiotemporal variability QL752.R48 Animal populations--Mathematical models Markov processes Monte Carlo method Bayesian statistical decision theory Common murre--Scotland--May Isle of Reynolds, Toby J. Bayesian modelling of integrated data and its application to seabird populations |
topic_facet |
Bayesian inference Common guillemot (Uria aalge) Integrated population model Markov chain Monte Carlo (MCMC) Multi-population dynamics Projections Spatiotemporal variability QL752.R48 Animal populations--Mathematical models Markov processes Monte Carlo method Bayesian statistical decision theory Common murre--Scotland--May Isle of |
description |
Integrated 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 ... |
author2 |
King, Ruth Harwood, John Frederiksen, Morten Harris, Michael P. Wanless, S. (Sarah) |
format |
Doctoral or Postdoctoral Thesis |
author |
Reynolds, Toby J. |
author_facet |
Reynolds, Toby J. |
author_sort |
Reynolds, Toby J. |
title |
Bayesian modelling of integrated data and its application to seabird populations |
title_short |
Bayesian modelling of integrated data and its application to seabird populations |
title_full |
Bayesian modelling of integrated data and its application to seabird populations |
title_fullStr |
Bayesian modelling of integrated data and its application to seabird populations |
title_full_unstemmed |
Bayesian modelling of integrated data and its application to seabird populations |
title_sort |
bayesian modelling of integrated data and its application to seabird populations |
publisher |
University of St Andrews |
publishDate |
2010 |
url |
http://hdl.handle.net/10023/1635 |
op_coverage |
180 p. |
genre |
common guillemot Common Murre Uria aalge uria |
genre_facet |
common guillemot Common Murre Uria aalge uria |
op_relation |
Reynolds, 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. http://hdl.handle.net/10023/1635 |
op_rights |
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported http://creativecommons.org/licenses/by-nc-nd/3.0/ |
_version_ |
1770271480651710464 |