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Estimation methods for nonlinear state-space models in ecology

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  • Pedersen, M.W.
  • Berg, C.W.
  • Thygesen, U.H.
  • Nielsen, A.
  • Madsen, H.

Abstract

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:8:p:1394-1400
    DOI: 10.1016/j.ecolmodel.2011.01.007
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    References listed on IDEAS

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    1. 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.
    2. Wolfinger, Russell D. & Xihong Lin, 1997. "Two Taylor-series approximation methods for nonlinear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 465-490, September.
    3. Skaug, Hans J. & Fournier, David A., 2006. "Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 699-709, November.
    4. 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.
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    Cited by:

    1. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    2. de Ávila-Simas, Sunshine & Morato, Marcelo M. & Reynalte-Tataje, David A. & Silveira, Hector B. & Zaniboni-Filho, Evoy & E. Normey-Rico, Julio, 2019. "Model-based predictive control for the regulation of the golden mussel Limnoperna fortunei (Dunker, 1857)," Ecological Modelling, Elsevier, vol. 406(C), pages 84-97.
    3. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2017. "Reprint of: Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 359(C), pages 461-467.
    4. Zheng, Nan & Cadigan, Noel, 2021. "Frequentist delta-variance approximations with mixed-effects models and TMB," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    5. Solbu, Erik Blystad & Engen, Steinar & Diserud, Ola Håvard, 2015. "Guidelines when estimating temporal changes in density dependent populations," Ecological Modelling, Elsevier, vol. 313(C), pages 355-376.
    6. Simone Vincenzi & Marc Mangel & Alain J Crivelli & Stephan Munch & Hans J Skaug, 2014. "Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-16, September.

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