Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

Methods in Ecology and Evolution

Volumn 2, Issue 6, 2011, Pages 595-601

  Martin, Julien ^a   Royle, J Andrew ^b   Mackenzie, Darryl I ^c   Edwards, Holly H ^a   Kéry, Marc ^d   Gardner, Beth ^e  

^a FISH AND WILDLIFE RESEARCH INSTITUTE   (United States)

^b PATUXENT WILDLIFE RESEARCH CENTER   (United States)

^c Proteus Wildlife Research Consultants   (New Zealand)

^d SWISS ORNITHOLOGICAL INSTITUTE   (Switzerland)

^e NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Beta binomial distribution; Binomial mixture; Correlated behaviour; Correlated Bernoulli outcomes; Manatees; Truncated Poisson distribution

Indexed keywords

EID: 84255205893     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/j.2041-210X.2011.00113.x     Document Type: Article

References (21)

1. Dawson, D.K. & Efford, M.G. (2009) Bird population density estimated from acoustic signals. Journal of Applied Ecology, 46, 1201-1209.
2. Edwards, H.H., Pollock, K.H., Ackerman, B., Reynolds, J. & Powell, J. (2007) Components of detection probability in manatee aerial surveys during winter. Journal of Wildlife Management, 71, 2052-2060.
3. Gelman, A., Meng, X. & Stern, H. (1996) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica, 6, 733-807.
4. Gelman, A., Carlin, J., Stern, H. & Rubin, D. (2004) Bayesian Data Analysis. CRC/Chapman & Hall, Boca Raton, FL.
5. Hisakado, M., Kitsukawa, K. & Mori, S. (2006) Correlated binomial models and correlation structures. Journal of Physics A: Mathematical and General, 39, 15365-15378.
6. Johnson, F.A. (2010). Analysis and management of migratory bird populations in North America. PhD thesis, University of Florida, Gainesville, FL.
7. Kéry, M. (2008) Estimating abundance from bird counts: binomial mixture models to uncover complex covariate relationships. The Auk, 125, 336-345.
8. Kéry, M. & Schaub, M. (in press) Bayesian population analysis using WinBUGS. Academic Press, Burlington.
9. Link, W.A., Cam, E., Nichols, J.D. & Cooch, E.G. (2002) Of Bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research. Journal of Wildlife Management, 66, 277-291.
10. Lunn, D.J., Thomas, A., Best, N. & Spiegelhalter, D. (2000) WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, 325-337.
11. Martin, J., Kitchens, W.M. & Hines, J.E. (2007) Importance of well-designed monitoring programs for the conservation of endangered species: case study of the snail kite. Conservation Biology, 21, 472-481.
12. McClintock, B.T., Bailey, L.L., Pollock, K.H. & Simons, T.R. (2010) Unmodeled observation error induces bias when inferring patterns and dynamics of species occurence on auditory detections. Ecology, 91, 2446-2454.
13. Prentice, R. L. (1986) Discussion of Correlation Induced by Covariate Measurement Errors. Journal of the American Statistical Association, 81, 321-327.
14. R Development Core Team (2010) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. URL http://www.R-project.org (accessed 1 July 2010).
15. Royle, J.A. (2004a) N-mixture models for estimating population size from spatially replicated counts. Biometrics, 60, 108-115.
16. Royle, J.A. (2004b) Generalized estimators of avian abundance from count survey data. Animal Biodiversity and Conservation, 27, 375-386.
17. Royle, J.A. & Dorazio, R.M. (2008) Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Academic Press, San Diego, CA.
18. Royle, J.A., Nichols, J.D. & Kéry, M. (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos, 110, 353-359.
19. Sturtz, S., Ligges, U. & Gelman, A. (2005) R2WinBUGS:a package for running WinBUGS from R. Journal of Statistical Software, 12, 1-16.
20. Wenger, S.J. & Freeman, M.C. (2008) Estimating species occurrence, abundance, and detection probability using zero-inflated distributions. Ecology, 89, 2953-2959.
21. Williams, B.K., Nichols, J.D. & Conroy, M.J. (2002) Analysis and Management of Animal Populations. Academic Press edn., San Diego, CA.