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Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK

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  • Ruth King
  • Stephen P. Brooks
  • Chiara Mazzetta
  • Stephen N. Freeman
  • Byron J. T. Morgan

Abstract

Summary. We combine data from separate ring recovery and survey studies to provide indices of estimated abundance for the UK lapwing Vanellus vanellus population. Using a descriptive state space model, we demonstrate the observed decline in population size in relation to directly interpretable parameters describing the demographic characteristics of the population. The Bayesian approach readily provides information that is directly relevant to the conservation of this important bird species. The method proposed extends previous work in this area in several ways. Restrictive normality assumptions that have traditionally been imposed are removed, in addition to the assumption of constant measurement error by using information relating to the index variability across time to account fully for this source of uncertainty within the model. We also provide model‐averaged inference to help to inform management policy and uses.

Suggested Citation

  • Ruth King & Stephen P. Brooks & Chiara Mazzetta & Stephen N. Freeman & Byron J. T. Morgan, 2008. "Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 609-632, December.
    • Handle: RePEc:bla:jorssc:v:57:y:2008:i:5:p:609-632
    DOI: 10.1111/j.1467-9876.2008.00633.x
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    References listed on IDEAS

    as
    1. R. King & S. P. Brooks, 2002. "Model Selection for Integrated Recovery/Recapture Data," Biometrics, The International Biometric Society, vol. 58(4), pages 841-851, December.
    2. P. Besbeas & S. N. Freeman & B. J. T. Morgan & E. A. Catchpole, 2002. "Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters," Biometrics, The International Biometric Society, vol. 58(3), pages 540-547, September.
    3. Chiara Mazzetta & Steve Brooks & Stephen N. Freeman, 2007. "On Smoothing Trends in Population Index Modeling," Biometrics, The International Biometric Society, vol. 63(4), pages 1007-1014, December.
    4. Russell B. Millar & Renate Meyer, 2000. "Non‐linear state space modelling of fisheries biomass dynamics by using Metropolis‐Hastings within‐Gibbs sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 327-342.
    5. S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & S. C. Barry, 2000. "On the Bayesian Analysis of Ring-Recovery Data," Biometrics, The International Biometric Society, vol. 56(3), pages 951-956, September.
    6. S. C. Barry & S. P. Brooks & E. A. Catchpole & B. J. T. Morgan, 2003. "The Analysis of Ring-Recovery Data Using Random Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 54-65, March.
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    Cited by:

    1. R. B. Millar & S. McKechnie, 2014. "A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models," Biometrics, The International Biometric Society, vol. 70(4), pages 972-980, December.
    2. Besbeas, P.T. & McCrea, R.S. & Morgan, B.J.T., 2022. "Selecting age structure in integrated population models," Ecological Modelling, Elsevier, vol. 473(C).
    3. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    4. Blanca Sarzo & Ruth King & David Conesa & Jonas Hentati-Sundberg, 2021. "Correcting Bias in Survival Probabilities for Partially Monitored Populations via Integrated Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 200-219, June.

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