An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

Abstract

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

Keywords:

• Bayesian method
• N-mixture model
• abundance estimation
• MCMC

AMS Subject Classification :

• 62F15
• 62P12
• 91-08

Acknowledgements

We thank Steve Gutreuter and Fanglong Dong for helpful reviews of an early draft of this manuscript. We also thank the two anonymous reviewers for their helpful comments and suggestions. The use of product or firm names does not imply endorsement by the US Government.

Additional information

Notes on contributors

S. G. Toribio

The Contributions of S.G. Toribio, S. Liang appear by permission of the United States Geological Survey, and was undertaken as part of Requisition/Reference No. 0722827038, Order No: 07ERSA0429.

S. Liang

The Contributions of S.G. Toribio, S. Liang appear by permission of the United States Geological Survey, and was undertaken as part of Requisition/Reference No. 0722827038, Order No: 07ERSA0429.