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

Journal of Statistical Computation and Simulation
By: , and 

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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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data
Series title Journal of Statistical Computation and Simulation
DOI 10.1080/00949655.2011.572881
Volume 82
Issue 8
Year Published 2011
Language English
Publisher Taylor & Francis
Publisher location Philadelphia, PA
Contributing office(s) Upper Midwest Environmental Sciences Center
Description 9 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Statistical Computation and Simulation
First page 1135
Last page 1143