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Reprint of: Fitting population growth models in the presence of measurement and detection error


  • Hefley, Trevor J.
  • Tyre, Andrew J.
  • Blankenship, Erin E.


Population time series data from field studies are complex and statistical analysis requires models that describe nonlinear population dynamics and observational errors. State-space formulations of stochastic population growth models have been used to account for measurement error caused by the data collection process. Parameter estimation, inference, and prediction are all sensitive to measurement error. The observational process may also result in detection errors and if unaccounted for will result in biased parameter estimates. We developed an N-mixture state-space modeling framework to estimate and correct for errors in detection while estimating population model parameters. We tested our methods using simulated data sets and compared the results to those obtained with state-space models when detection is perfect and when detection is ignored. Our N-mixture state-space model yielded parameter estimates of similar quality to a state-space model when detection is perfect. Our results show that ignoring detection errors can lead to biased parameter estimates including an overestimated growth rate, underestimated equilibrium population size and estimated population state that is misleading. We recommend that researchers consider the possibility of detection errors when collecting and analyzing population time series data.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:359:y:2017:i:c:p:461-467
    DOI: 10.1016/j.ecolmodel.2013.10.021

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    References listed on IDEAS

    1. Péter Sólymos & Subhash Lele & Erin Bayne, 2012. "Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error," Environmetrics, John Wiley & Sons, Ltd., vol. 23(2), pages 197-205, March.
    2. Little, Roderick J., 2006. "Calibrated Bayes: A Bayes/Frequentist Roadmap," The American Statistician, American Statistical Association, vol. 60, pages 213-223, August.
    3. 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.
    4. Lele, Subhash R. & Nadeem, Khurram & Schmuland, Byron, 2010. "Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1617-1625.
    5. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
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