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Age Specificity in Conditional Ring-Recovery Models

  • Chiara Mazzetta
Article

Abstract

We consider the case of age-specific ring-recovery data obtained only from recovered individual birds and modelled by conditioning a multinomial distribution on the recovery. These models may be appealing when the information about the numbers of marked individuals is missing, but they have previously been analyzed by ignoring a large set of nuisance parameters, the recovery probabilities. We investigate the consequences of this conditioning by relating the age-time specific structure of recovery probabilities to the estimation of survival.

Key Words

Bayesian estimation Conditional multinomial MCMC Survival probabilities 

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References

  1. Besbeas, P., Freeman, S. N., Morgan, B. J. T., and Catchpole, E. A. (2002), “Integrating Mark-Recapture-Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters,” Biometrics, 58, 540–547. MathSciNetCrossRefzbMATHGoogle Scholar
  2. Brooks, S. P., King, R., and Morgan, B. J. T. (2004), “A Bayesian Approach to Combining Animal Abundance and Demographic Data,” Animal Biodiversity and Conservation, 27, 515–529. Google Scholar
  3. Burnham, K. P. (1990), “Survival Analysis of Recovery Data From Birds Ringed as Young: Efficiency of Analyses when Numbers Ringed Are Not Known,” The Ring, 13, 115–132. Google Scholar
  4. Catchpole, E. A., and Morgan, B. J. T. (1997), “Detecting Parameter Redundancy,” Biometrika, 84, 187–196. MathSciNetCrossRefzbMATHGoogle Scholar
  5. Catchpole, E. A., Morgan, B. J. T., and Freeman, S. N. (1998), “Estimation in Parameter-Redundant Models,” Biometrika, 85, 462–468. MathSciNetCrossRefzbMATHGoogle Scholar
  6. Catchpole, E. A., Morgan, B. J. T., and Coulson, T. (2004), “Conditional Methodology for Individual Case History Data,” Applied Statistics, 53, 123–131. MathSciNetzbMATHGoogle Scholar
  7. Chib, S., and Greenberg, E. (1995), “Understanding the Metropolis–Hastings Algorithm,” The American Statistician, 49, 327–335. Google Scholar
  8. Freeman, S. N., and Morgan, B. J. T. (1992), “A Modelling Strategy for Recovery Data from Birds Ringed as Nestlings,” Biometrics, 48, 217–235. CrossRefGoogle Scholar
  9. Freeman, S. N., Robinson, R. A., Clark, J. A., Griffin, B. M., and Adams, S. Y. (2007), “Changing Demography and Population Decline in the Common Starling Sturnus Vulgaris: A Multisite Approach to Integrated Population Monitoring,” Ibis, 149, 587–596. CrossRefGoogle Scholar
  10. Gelman, A., Roberts, G. O., and Gilks, W. R. (1996), “Efficient Metropolis Jumping Rules,” in BAYESIAN STATISTICS 5, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, Oxford: OUP, pp. 599–607. Google Scholar
  11. Marchant, J. H., Leech, D. I., Joys, A. C., Noble, D. G., Barimore, C., Grantham, M. J., Riseley, K., and Robinson, R. A. (2009), “Breeding Birds in the Wider Countryside: Their Conservation Status 2008,” Technical Report No. 516, BTO, Thetford, England. Google Scholar
  12. Mazzetta, C., Brooks, S. N., and Freeman, S. N. (2007), “On Smoothing Trends in Population Index Modelling,” Biometrics, 67, 1007–1014. MathSciNetCrossRefzbMATHGoogle Scholar
  13. Thomas, L., Buckland, S. T., Newman, K. B., and Harwood, J. (2005), “A Unified Framework for Modelling Wildlife Population Dynamics,” Australian and New Zealand Journal of Statistics, 47, 19–34. MathSciNetCrossRefzbMATHGoogle Scholar
  14. White, G. C., and Burnham, K. P. (1999), “Program Mark: Survival Estimation From Population of Marked Animals,” Bird Study, 46 (Suppl.), S120–S139. CrossRefGoogle Scholar
  15. Williams, B. K., Nichols, J. D., and Conroy, M. J. (2002), Analysis and Management of Animal Populations, San Diego: Academic Press. Google Scholar

Copyright information

© International Biometric Society 2010

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of WarwickCoventryEngland

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