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
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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.
Suggested Citation
Toribo, S., Gray, B., Liang, S., 2011, 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, v. 82, no. 8, p. 1135-1143, https://doi.org/10.1080/00949655.2011.572881.
| 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 |