• Albert, J.H. & Chib, S. (1993) Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669679.
  • Dorazio, R.M., Kéry, M., Royle, J.A. & Plattner, M. (2010) Models for inference in dynamic metacommunity systems. Ecology, 91, 24662475.
  • Dorazio, R.M., Gotelli, N.J. & Ellison, A.M. (2011) Modern methods of estimating biodiversity from presence-absence surveys. Biodiversity Loss in a Changing Planet (eds O. Grillo & G. Venora), pp. 277302. InTech, Rijeka, Croatia.
  • Elith, J. & Leathwick, J.R. (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677697.
  • Fiske, I. & Chandler, R. (2011) unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software, 43, 123.
  • Flegal, J.M. & Jones, G.L. (2010) Batch means and spectral variance estimators in Markov chain Monte Carlo. Annals of Statistics, 38, 10341070.
  • Flegal, J.M. & Jones, G.L. (2011) Implementing MCMC: estimating with confidence. Handbook of Markov chain Monte Carlo (eds S. Brooks, A. Gelman, G.L. Jones & X.L. Meng), pp. 175197. Chapman & Hall/CRC, Boca Raton, Florida, USA.
  • Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2004) Bayesian data analysis, 2nd edn. Chapman and Hall, Boca Raton, Florida, USA.
  • Geyer, C.J. (2011) Introduction to Markov chain Monte Carlo. Handbook of Markov chain Monte Carlo (eds S. Brooks, A. Gelman, G.L. Jones & X.L. Meng), pp. 348. Chapman & Hall/CRC, Boca Raton, Florida, USA.
  • Hanski, I. & Gilpin, M.E. (eds), (1997) Metapopulation Biology: Ecology, Genetics, and Evolution. Academic Press, New York.
  • Kéry, M. (2010) Introduction to WinBUGS for Ecologists. Academic Press, Burlington, Massachusetts, USA.
  • Kéry, M. & Schaub, M. (2012) Bayesian Population Analysis Using WinBUGS. Academic Press, Waltham, Massachusetts, USA.
  • Kéry, M., Gardner, B. & Monnerat, C. (2010) Predicting species distributions from checklist data using site-occupancy models. Journal of Biogeography, 37, 18511862.
  • Laird, N.M. & Louis, T.A. (1987) Empirical Bayes confidence intervals based on bootstrap samples (with discussion). Journal of the American Statistical Association, 82, 739757.
  • Link, W.A. & Barker, R.J. (2010) Bayesian Inference. Academic Press, Amsterdam.
  • Liu, J.S. & Wu, Y.N. (1999) Parameter expansion for data augmentation. Journal of the American Statistical Association, 94, 12641274.
  • MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A. & Langtimm, C.A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83, 22482255.
  • MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L. & Hines, J.E. (2006) Occupancy Estimation and Modeling. Elsevier, Amsterdam.
  • R Development Core Team. (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
  • Royle, J.A. & Dorazio, R.M. (2008) Hierarchical Modeling and Inference in Ecology. Academic Press, Amsterdam.
  • Royle, J.A. & Kéry, M. (2007) A Bayesian state-space formulation of dynamic occupancy models. Ecology, 88, 18131823.
  • Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B. (2002) Predicting Species Occurrences: Issues of Accuracy and Scale. Island Press, Washington, DC.
  • Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Parris, K. & Possingham, H.P. (2003) Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications, 13, 17901801.