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State-space modelling of data on marked individuals

Author

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  • Gimenez, Olivier
  • Rossi, Vivien
  • Choquet, Rémi
  • Dehais, Camille
  • Doris, Blaise
  • Varella, Hubert
  • Vila, Jean-Pierre
  • Pradel, Roger

Abstract

State-space models have recently been proposed as a convenient and flexible framework for specifying stochastic models for the dynamics of wild animal populations. Here we focus on the modelling of data on marked individuals which is frequently used in order to estimate demographic parameters while accounting for imperfect detectability. We show how usual models to deal with capture–recapture and ring-recovery data can be fruitfully written as state-space models. An illustration is given using real data and a Bayesian approach using MCMC methods is implemented to estimate the parameters. Eventually, we discuss future developments that may be facilitated by the SSM formulation.

Suggested Citation

  • Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    • Handle: RePEc:eee:ecomod:v:206:y:2007:i:3:p:431-438
    DOI: 10.1016/j.ecolmodel.2007.03.040
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    1. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    2. Wang, Guiming, 2007. "On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models," Ecological Modelling, Elsevier, vol. 200(3), pages 521-528.
    3. Huggins, Richard, 2001. "A note on the difficulties associated with the analysis of capture-recapture experiments with heterogeneous capture probabilities," Statistics & Probability Letters, Elsevier, vol. 54(2), pages 147-152, September.
    4. P. Besbeas & J.‐D. Lebreton & B. J. T. Morgan, 2003. "The efficient integration of abundance and demographic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 95-102, January.
    5. 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.
    6. Kenneth Pollock, 2002. "The use of auxiliary variables in capture-recapture modelling: An overview," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 85-102.
    7. 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.
    8. J. D. Lebreton & R. Pradel Cefe, 2002. "Multistate recapture models: Modelling incomplete individual histories," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 353-369.
    9. Roger Pradel, 2005. "Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States," Biometrics, The International Biometric Society, vol. 61(2), pages 442-447, June.
    10. de Valpine P., 2004. "Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 523-536, January.
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    4. Vila, Jean-Pierre, 2012. "Enhanced consistency of the Resampled Convolution Particle Filter," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 786-797.
    5. Bird, Tomas & Lyon, Jarod & Wotherspoon, Simon & King, Ruth & McCarthy, Michael, 2017. "Accounting for false mortality in telemetry tag applications," Ecological Modelling, Elsevier, vol. 355(C), pages 116-125.
    6. Diana J. Cole, 2019. "Parameter redundancy and identifiability in hidden Markov models," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 105-118, August.
    7. Sigourney, Douglas B. & Munch, Stephan B. & Letcher, Benjamin H., 2012. "Combining a Bayesian nonparametric method with a hierarchical framework to estimate individual and temporal variation in growth," Ecological Modelling, Elsevier, vol. 247(C), pages 125-134.
    8. Pedersen, M.W. & Berg, C.W. & Thygesen, U.H. & Nielsen, A. & Madsen, H., 2011. "Estimation methods for nonlinear state-space models in ecology," Ecological Modelling, Elsevier, vol. 222(8), pages 1394-1400.
    9. Choquet, Rémi & Garnier, Alexandre & Awuve, Edem & Besnard, Aurélien, 2017. "Transient state estimation using continuous-time processes applied to opportunistic capture–recapture data," Ecological Modelling, Elsevier, vol. 361(C), pages 157-163.
    10. Karavarsamis, N. & Huggins, R.M., 2019. "Two-stage approaches to the analysis of occupancy data II. The heterogeneous model and conditional likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 195-207.
    11. Gimenez, Olivier & Mansilla, Lorena & Klaich, M. Javier & Coscarella, Mariano A. & Pedraza, Susana N. & Crespo, Enrique A., 2019. "Inferring animal social networks with imperfect detection," Ecological Modelling, Elsevier, vol. 401(C), pages 69-74.
    12. Gimenez, Olivier & Lebreton, Jean-Dominique & Gaillard, Jean-Michel & Choquet, Rémi & Pradel, Roger, 2012. "Estimating demographic parameters using hidden process dynamic models," Theoretical Population Biology, Elsevier, vol. 82(4), pages 307-316.

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