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A Bayesian approach to estimating animal density from binary acoustic transects

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  • Horrocks, Julie
  • Rueffer, Matthew

Abstract

A Bayesian model is proposed for estimating abundance or density of animals from passive acoustic binary data. The data are collected at points along one or more transects, and the points are spaced so that a single individual can be heard multiple times. Thus successive data points are dependent and this dependence is exploited to simultaneously estimate density, range of detection and probability of detection. The data are assumed to follow a homogeneous Poisson process. The Bayesian model combines a prior distribution for the model parameters, with a second-order Markov approximation to the likelihood. Sensitivity of the model to choice of priors is investigated. The method is illustrated using acoustic data from a survey of sperm whales (Physeter macrocephalus).

Suggested Citation

  • Horrocks, Julie & Rueffer, Matthew, 2014. "A Bayesian approach to estimating animal density from binary acoustic transects," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 17-25.
    • Handle: RePEc:eee:csdana:v:80:y:2014:i:c:p:17-25
    DOI: 10.1016/j.csda.2014.06.005
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    References listed on IDEAS

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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).
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    3. Julie Horrocks & David C. Hamilton & Hal Whitehead, 2011. "A Likelihood Approach to Estimating Animal Density from Binary Acoustic Transects," Biometrics, The International Biometric Society, vol. 67(3), pages 681-690, September.
    4. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
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