Bayesian analysis for population ecology

Bayesian Analysis for Population Ecology

Volumn , Issue , 2009, Pages 1-442

  King, Ruth ^a   Morgan, Byron J T ^b   Gimenez, Olivier ^c   Brooks, Stephen P ^d  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b UNIVERSITY OF KENT   (United Kingdom)

^c CNRS   (France)

^d ATASS Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85056343555     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (294)

1. Aebischer, N. J. (1999), Multi-way comparisons and generalised linear models of nest success: Extensions of the Mayfield method. Bird Study 46 (suppl), 22-31
2. Al-Awadhi, F., Hurn, M. and Jennison, C. (2004), Improving the acceptance rate of reversible jump MCMC proposals. Statistics and Probability Letters 69, 189-198
3. Albert, J. (2007), Bayesian computation with R. Springer, New York
4. Anderson-Sprechler, R. and Ledolte, J. (1991), State-space analysis of wildlife telemetry data. Journal of the American Statistical Association 86, 596-602
5. Arnold, R., Hayakawa, Y. and Yip, P. (2009), Capture-recapture estimation using finite mixtures of arbitrary dimension. Biometrics – in press
6. Baillie, S. R., Brooks, S. P., King, R. and Thomas, L. (2009), Using a statespace model of the British song thrush Turdus philomelos population to diagnose the causes of a population decline. In D. L. Thomson, E. G. Cooch and M. J. Conroy (eds.), Modeling Demographic Processes in Marked Populations, Springer - Series: Environmental and Ecological Statistics, Volume 3, pp. 543-564
7. Bairlein, F. (1991), Population studies of white storks Ciconia ciconia in Europe, with reference to the western population. In C. Perrins, J. D. Lebreton and G. Hirons (eds.), Bird population studies: Relevance to conservation and management, Oxford University Press, Oxford, pp. 207-229
8. Barbraud, C., Barbraud, J. C. and Barbraud, M. (1999), Population dynamics of the white stork Ciconia ciconia in western France. Ibis 141, 469-479
9. Barker, R. J. (1997), Joint modelling of live recapture, tag-resight and tagrecovery data. Biometrics 53, 666-677
10. Barker, R. J. (1999), Joint analysis of mark-recapture, resighting and ring-recovery data with age-dependence and marking-effect. Bird Study 46 (suppl), 82-91
11. Barry, S. C., Brooks, S. P., Catchpole, E. A. and Morgan, B. J. T. (2003), The analysis of ring-recovery data using random effects. Biometrics 59, 54-65
12. Bartolucci, F. and Forcina, A. (2001), Analysis of capture-recapture data with a Rasch-type model allowing for conditional dependence and multidimensionality. Biometrics 57, 714-719
13. Bayarri, M. J. and Berger, J. O. (1998), Quantifying surprise in the data and model verification. In J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds.), Bayesian statistics 6, Oxford University Press, Oxford, pp. 53-82
14. Bayes, T. (1763), An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London 53, 370-418
15. Bellhouse, D. R. (2004), The Reverend Thomas Bayes, FRS: A biography to celebrate the tercentenary of his birth. Statistical Science 19, 3-43
16. Bellman, R. and Åström, K. J. (1970), On structural identifiability. Mathematical Biosciences 7, 329-339
17. Besbeas, P., Freeman, S. N. and Morgan, B. J. T. (2005), The potential of integrated population modelling. Australian and New Zealand Journal Statistics 46, 35-48
18. Besbeas, P., Freeman, S. N., Morgan, B. J. T. and Catchpole, E. A. (2001), Stochastic models for animal abundance and demographic data. Technical Report UKC/IMS/01/16, University of Kent, Canterbury, England
19. Besbeas, P., Freeman, S. N., Morgan, B. J. T. and Catchpole, E. A. (2002), Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58, 540-547
20. Besbeas, P., Lebreton, J. D. and Morgan, B. J. T. (2003), The efficient integration of abundance and demographic data. Journal of the Royal Statistical Society, Series C 52, 95-102
21. Bolker, B. M. (2008), Ecological models and data in R. Princeton University Press, Princeton, NJ
22. Bonner, S. J., Morgan, B. J. T. and King, R. (2009), Continuous covariates in mark-recapture-recovery analysis: A comparison of methods. Technical report, Simon Fraser University
23. Bonner, S. J. and Schwarz, C. J. (2006), An extension of the Cormack-Jolly-Seber model for continuous covariates with application to Microtus Pennsylvanicus. Biometrics 62, 142-149
24. Borchers, D. L., Buckland, S. T., King, R., Newman, K. B., Thomas, L., Besbeas, P. T., Gimenez, O. and Morgan, B. J. T. (2010), Estimating Animal Abundance: Open Populations. In preparation
25. Borchers, D. L., Buckland, S. T. and Zucchini, W. (2002), Estimating Animal Abundance: Closed Populations. Springer, London
26. Borysiewicz, R. S., Gimenez, O., Hénaux, V., Bregnballe, T., Lebreton, J.-D. and Morgan, B. J. T. (2009), An integrated analysis of multisite recruitment, mark-recapture-recovery and multisite census data. In D. L. Thomson, E. G. Cooch and M. J. Conroy (eds.), Modeling Demographic Processes in Marked Populations, Springer - Series: Environmental and Ecological Statistics, Volume 3, pp. 1055-1067
27. Brooks, S. P. (2003), Bayesian computation: A statistical revolution. Transactions of the Royal Society, Series A 361, 2681-2697
28. Brooks, S. P., Catchpole, E. A. and Morgan, B. J. T. (2000a), Bayesian animal survival estimation. Statistical Science 15, 357-376
29. Brooks, S. P., Catchpole, E. A., Morgan, B. J. T. and Barry, S. C. (2000b), On the Bayesian analysis of ring-recovery data. Biometrics 56, 951-956
30. Brooks, S. P., Catchpole, E. A., Morgan, B. J. T. and Harris, M. P. (2002), Bayesian methods for analysing ringing data. Journal of Applied Statistics. 29, 187-206
31. Brooks, S. P., Friel, N. and King, R. (2003a), Classical model selection via simulated annealing. Journal of the Royal Statistical Society, Series B 65, 503-520
32. Brooks, S. P. and Gelman, A. (1998), Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434-455
33. Brooks, S. P., Giudici, P. and Roberts, G. O. (2003b), Efficient construction of reversible jump MCMC proposal distributions (with discussion). Journal of the Royal Statistical Society, Series B 65, 3-55
34. Brooks, S. P. and Guidici, P. (2000), MCMC convergence assessment via two-way ANOVA. Journal of Computational and Graphical Statistics 9, 266-285
35. Brooks, S. P., King, R. and Morgan, B. J. T. (2004), A Bayesian approach to combining animal abundance and demographic data. Animal Biodiversity and Conservation 27, 515-529
36. Brooks, S. P. and Morgan, B. J. T. (1995), Optimization using simulated annealing. Statistician 44, 241-257
37. Brooks, S. P. and Roberts, G. O. (1999), On quantile estimation and MCMC convergence. Biometrika 86, 710-717
38. Brownie, C., Anderson, D. R., Burnham, K. P. and Robson, D. S. (1985), Statistical inference from band recovery data: A handbook. U.S. Fish and Wildlife Resource Publication 156
39. Brownie, C., Hines, J. E., Nichols, J. D., Pollock, K. H. and Hestbeck, J. B. (1993), Capture-recapture studies for multiple strata including non-Markovian transitions. Biometrics 49, 1173-1187
40. Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. and Thomas, L. (2001), Introduction to distance sampling. Oxford University Press, Oxford
41. Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. and Thomas, L. (eds.) (2004a), Advanced distance sampling. Oxford University Press, Oxford
42. Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997), Model selection: an integral part of inference. Biometrics 53, 603-618
43. Buckland, S. T., Newman, K. N., Fernández, C., Thomas, L. and Harwood, J. (2007), Embedding population dynamics models in inference. Statistical Science 22, 44-58
44. Buckland, S. T., Newman, K. N., Thomas, L. and Koesters, N. B. (2004b), State-space models for the dynamics of wild animal populations. Ecological Modelling 171, 157-175
45. Burnham, K. P. (1993), A theory for combined analysis of ring-recovery and recapture data. In J.-D. Lebreton and P. M. North (eds.), Marked individuals in the study of bird populations, Birkhäuser Verlag, Basel, pp. 199-213
46. Cameron, C., Barker, R. J., Fletcher, D., Slooten, E. and Dawson, S. (1999), Modelling survival of Hector’s dolphins around Banks peninsula, New Zealand. Journal of Agricultural, Biological, and Environmental Statistics 4, 126-135
47. Carlin, B. P. and Chib, S. (1995), Bayesian model choice via Markov chain Monte Carlo. Journal of the Royal Statistical Society, Series B 57, 473-484
48. Carlin, B. P. and Louis, T. A. (1996), Bayes and empirical Bayes methods for data analysis. Chapman & Hall, London
49. Carlin, B. P. and Louis, T. A. (2001), Bayes and empirical Bayes methods for data analysis. Chapman & Hall, Boca Raton, second edition
50. Carothers, A. D. (1973), Capture-recapture methods applied to a population with known parameters. Journal of Animal Ecology 42, 125-46
51. Casella, G. and George, E. I. (1992), Explaining the Gibbs sampler. Journal of the American Statistical Association 46, 167-174
52. Casella, G. and Robert, C. P. (1996), Rao-Blackwellization of sampling schemes. Biometrika 83, 81-94
53. Castledine, B. J. (1981), A Bayesian analysis of multiple-recapture sampling of a closed population. Biometrika 68, 197-210
54. Caswell, H. (2001), Matrix population models. Sinauer Associates, Sunderland, MA, second edition
55. Catchpole, E. A. (1995), Matlab: An environment for analyzing ring-recovery and recapture data. Journal of Applied Statistics 22, 801-816
56. Catchpole, E. A., Fan, Y., Morgan, B. J. T., Clutton-Brock, T. H. and Coulson, T. (2004), Sexual dimorphism, survival and dispersal in red deer. Journal of Agricultural, Biological, and Environmental Statistics 9, 1-26
57. Catchpole, E. A., Freeman, S. N. and Morgan, B. J. T. (1995), Modelling age variation in survival and reporting rates for recovery models. Journal of Applied Statistics 22, 597-609
58. Catchpole, E. A., Freeman, S. N. and Morgan, B. J. T. (1996), Steps to parameter redundancy in age-dependent recovery models. Journal of the Royal Statistical Society, Series B 58, 763-774
59. Catchpole, E. A., Freeman, S. N., Morgan, B. J. T. and Harris, M. P. (1998), Integrated recovery/recapture data analysis. Biometrics 54, 33-46
60. Catchpole, E. A., Freeman, S. N., Morgan, B. J. T. and Nash, W. J. (2001), Abalone I: Analysing mark-recapture-recovery data, incorporating growth and delayed recovery. Biometrics 57, 469-477
61. Catchpole, E. A. and Morgan, B. J. T. (1996), Model selection in ringrecovery models using score tests. Biometrics 52, 664-672
62. Catchpole, E. A. and Morgan, B. J. T. (1997), Detecting parameter redundancy. Biometrika 84, 187-196
63. Catchpole, E. A. and Morgan, B. J. T. (2001), Deficiency of parameterredundant models. Biometrika 88, 593-598
64. Catchpole, E. A., Morgan, B. J. T., Coulson, T. N., Freeman, S. N. and Albon, S. D. (2000), Factors inuencing Soay sheep survival. Journal of the Royal Statistical Society, Series C 49, 453-472
65. Catchpole, E. A., Morgan, B. J. T., Freeman, S. N. and Peach, W. J. (1999), Modelling the survival of British lapwings Vanellus vanellus using weather covariates. Bird Study 46 (suppl), 5-13
66. Catchpole, E. A., Morgan, B. J. T. and Tavecchia, G. (2008), A new method for analysing discrete life history data with missing covariate values. Journal of the Royal Statistical Society, Series B 70, 445-460
67. Catchpole, E. A., Morgan, B. J. T. and Viallefont, A. (2002), Solving problems in parameter redundancy using computer algebra. Journal of Applied Statistics, 29, 625-636
68. Cave, V. M., Freeman, S. N., Brooks, S. P., King, R. and Balmer, D. E. (2009a), On adjusting for missed visits in the indexing of abundance from Constant Effort ringing. In D. L. Thomson, E. G. Cooch and M. J. Conroy (eds.), Modeling Demographic Processes in Marked Populations, Springer - Series: Environmental and Ecological Statistics, Volume 3, pp. 951-966
69. Cave, V. M., King, R. and Freeman, S. N. (2009b), An integrated population model from constant effort bird-ringing data. Journal of Agricultural, Biological, and Environmental Statistics - in press
70. Celeux, G., Forbes, F., Robert, C. P. and Titterington, D. M. (2006), Deviance information criteria for missing data models (with discussion). Bayesian Analysis 1, 651-705
71. Chao, A., Tsay, P., Lin, S., Shau, W. and Chao, D. (2001), Tutorial in biostatistics: The applications of capture-recapture models to epidemiological data. Statistics in Medicine 20, 2123-3157
72. Chatfield, C. (1995), Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society, Series A 158(3), 419-466
73. Chib, S. and Greenberg, E. (1995), Understanding the Metropolis-Hastings algorithm. The American Statistician 49, 327-335
74. Chilvers, B. L., Wilkinson, I. S. and Mackenzie, D. I. (2009), Predicting life-history traits for female New Zealand sea lions, Phocartos hookeri: Integrating short-term mark-recapture data and population modelling. Journal of Agricultural, Biological, and Environmental Modelling - in press
75. Clark, J. S., Ferraz, G., Oguge, N., Hays, H. and Dicostanzo, J. (2005), Hierarchical Bayes for structured, variable populations: From recapture data to life-history prediction. Ecology 86, 2232-2244
76. Clobert, J. and Lebreton, J.-D. (1985), Dependance de facteurs de milieu dans les estimations de taux de survie par capture-recapture. Biometrics 41, 1031-1037
77. Clutton-Brock, T. H. (ed.) (1988), Reproductive success: Studies of individual variation in contrasting breeding systems. Chicago University Press, Chicago
78. Clutton-Brock, T. H., Guinness, F. E. and Albon, S. D. (1982), Red deer: Behaviour and ecology of two sexes. Chicago University Press, Chicago
79. Congdon, P. (2003), Applied Bayesian modelling. Wiley, Chichester
80. Congdon, P. (2006), Bayesian statistical modelling. Wiley, Chichester, second edition
81. Cormack, R. M. (1970), Statistical appendix to Fordham’s paper. Journal of Animal Ecology 39, 24-27
82. Coull, B. and Agresti, A. (1999), The use of mixed logit models to reflect heterogeneity in capture-recapture studies. Biometrics 55, 294-301
83. Coulson, T. N., Catchpole, E. A., Albon, S. D., Morgan, B. J. T., Pemberton, J. M., Clutton-Brock, T. H., Crawley, M. J. and Grenfell, B. T. (2001), Age, sex, density, winter weather, and population crashes in Soay sheep. Science 292, 1528-1531
84. Cowles, M. K. and Carlin, B. P. (1996), Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91, 883-904
85. Cox, D. R. and Oakes, D. (1984), Analysis of survival data. Chapman & Hall, London
86. Crainiceanu, C. M., Ruppert, D. and Wand, M. (2005), Bayesian analysis for penalized spline regression using WinBUGS. Journal of Statistical Software 14, 1-24
87. Cramp, S. and Simmons, K. E. L. (1983), The Birds of the Western Palearctic, Volume 3. Oxford University Press, Oxford
88. Crawley, M. J. (2007), The R book. Wiley, Chichester
89. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977), Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B 39, 1-38
90. Dennis, B. and Taper, M. L. (1994), Density dependence in time series observations of natural populations: Estimation and testing. Ecological Monographs 64, 205-224
91. Dorazio, R. M. and Royle, J. A. (2003), Mixture models for estimating the size of a closed population when capture rates vary among individuals. Biometrics 59, 351-364
92. Dorazio, R. M., Royle, J. A., Söderström, B. and Glimskär (2006), Estimating species richness and accumulation by modeling species occurrence and detectability. Ecology 87, 842-854
93. Dupuis, J. A. (1995), Bayesian estimation of movement and survival probabilities from capture-recapture data. Biometrika 82(4), 761-772
94. Dupuis, J. A., Badia, J., Maublanc, M. and Bon, R. (2002), Survival and spatial fidelity of mouons (Ovis gmelini): A Bayesian analysis of an agedependent capture-cecapture model. Journal of Agricultural, Biological, and Environmental Statistics 7, 277-298
95. Durban, J. W. and Elston, D. A. (2005), Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space. Journal of Agricultural, Biological, and Environmental Statistics 10, 291-305
96. Ellison, A. M. (2004), Bayesian inference in ecology. Ecology Letters 7, 509-520
97. Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie, S. R. and Wilson, J. D. (2000), Analysis of population trends for farmland birds using generalized additive models. Ecology 81, 1970-1984
98. Fienberg, S. E., Johnson, M. S. and Junker, B. W. (1999), Classical multilevel and Bayesian approaches to population size estimation using multiple lists. Journal of the Royal Statistical Society, Series A 162, 383-405
99. Fish and Wildlife Service (2003), Waterfowl Population Status. Technical report, Department of the Interior, Washington D.C.
100. Fisher, R. A. (1935), The design of experiments. Oliver and Boyd, London
101. Freeman, S. N. and Morgan, B. J. T. (1990), Studies in the analysis of ring-recovery data. The Ring 13, 271-288
102. Freeman, S. N. and Morgan, B. J. T. (1992), A modelling strategy for recovery data from birds ringed as nestlings. Biometrics 48, 217-236
103. Freeman, S. N., Morgan, B. J. T. and Catchpole, E. A. (1992), On the augmentation of ring-recovery data with field information. Journal of Animal Ecology 61, 649-657
104. Freeman, S. N. and North, P. M. (1990), Estimation of survival rates of British, Irish and French grey herons. The Ring 13, 139-165
105. Furness, R. W. and Greenwood, J. J. D. (1993), Birds as monitors of environmental change. Chapman & Hall, London
106. Gaillard, J.-M., Allaine, D., Pontier, D., Yoccoz, N. and Promislow, D. (1994), Senescence in natural populations of mammals: a reanalysis. Evolution 48(2), 509-516
107. Gamerman, D. (1997), Markov chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman & Hall, London
108. Geiger, D., Heckerman, D. and Meek, C. (1996), Asymptotic model selection for directed networks with hidden variables. In Proceedings of the twelfth conference on uncertainty in artificial intelligence, Morgan Kaufmann, San Mateo, CA, pp. 283-290
109. Gelfand, A. E. and Ghosh, S. K. (1998), Model choice: A minimum posterior predictive loss approach. Biometrika 85, 1-11
110. Gelman, A. (1996), Inference and monitoring convergence. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (eds.), Markov chain Monte Carlo in practice, Chapman & Hall, London, pp. 131-143
111. Gelman, A. (2006), Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515-534
112. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2003), Bayesian data analysis. Chapman & Hall, London, second edition
113. Gelman, A. and Hill, J. (2007), Data analysis using regression and multi-level/hierarchical models. Cambridge University Press, New York
114. Gelman, A., Jakulin, A., Pittau, M. G. and Su, Y.-S. (2008), A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics 2, 1360-1383
115. Gelman, A. and Meng, X. (1996), Model checking and model improvement. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (eds.), Markov chain Monte Carlo in practice, Chapman & Hall, London, pp. 189-201
116. Gelman, A., Roberts, G. O. and Gilks, W. R. (1996), Efficient Metropolis jumping rules. In J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds.), Bayesian statistics 5, Oxford University Press, Oxford, pp. 599-608
117. Gelman, A. and Rubin, D. B. (1992), Inference from iterative simulation using multiple sequences. Statistical Science 7, 457-511
118. Geweke, J. (1992), Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds.), Bayesian statistics 4, Oxford University Press, Oxford
119. Geyer, C. and Thompson, E. (1995), Annealing Markov chain Monte Carlo with applications to ancenstral inference. Journal of the American Statistical Association 90, 909-920
120. Ghosh, S. K. and Norris, J. L. (2005), Bayesian capture-recapture analysis and model selection allowing for heterogeneity and behavioural effects. Journal of Agricultural, Biological, and Environmental Statistics 10, 35-49
121. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996), Markov chain Monte Carlo in practice. Chapman & Hall, London
122. Gilks, W. R. and Roberts, G. O. (1996), Strategies for improving mcmc. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, pp. 89-114
123. Gilks, W. R., Thomas, D. and Spiegelhalter, D. J. (1992), Software for the Gibbs sampler. Computing Science and Statistics 24, 439-448
124. Gilks, W. R. and Wild, P. (1992), Adaptive rejection sampling for Gibbs sampling. Journal of the Royal Statistical Society, Series C 41, 337-348
125. Gimenez, O., Bonner, S. J., King, R., Parker, R. A., Brooks, S. P., Jamieson, L. E., Grosbois, V., Morgan, B. J. T. and Thomas, L. (2009a), Win- BUGS for population ecologists: Bayesian modelling using Markov chain Monte Carlo. In D. L. Thomson, E. G. Cooch and M. J. Conroy (eds.), Modeling Demographic Processes in Marked Populations, Springer - Series: Environmental and Ecological Statistics, Volume 3, pp. 885-918
126. Gimenez, O., Choquet, R. and Lebreton, J.-D. (2003), Parameter redundancy in multistate capture-recapture models. Biometrical J 45, 704-722
127. Gimenez, O., Covas, R., Brown, C. R., Anderson, M. D., Brown, M. B. and Lenormand, T. (2006a), Nonparametric estimation of natural selection on a quantitative trait using mark-recapture data. Evolution 60, 460-466
128. Gimenez, O., Crainiceanu, C., Barbraud, C., Jenouvrier, S. and Morgan, B. J. T. (2006b), Semiparametric regression in capture-recapture modelling. Biometrics 62, 691-698
129. Gimenez, O., Morgan, B. J. T. and Brooks, S. P. (2009b), Weak identifiability in models for mark-recapture-recovery data. In D. L. Thomson, E. G. Cooch and M. J. Conroy (eds.), Modeling Demographic Processes in Marked Populations, Springer - Series: Environmental and Ecological Statistics, Volume 3, pp. 1055-1067
130. Gimenez, O., Rossi, V., Choquet, R., Dehais, C., Doris, B., Varella, H., Vila, J.-P. and Pradel, R. (2007), State-space modelling of data on marked individuals. Ecological Modelling 206, 431-438
131. Gimenez, O., Viallefont, A., Catchpole, E. A., Choquet, R. and Morgan, B. J. T. (2004), Methods for investigating parameter redundancy. Animal Biodiversity and Conservation 27, 561-572
132. Givens, G. H. and Hoeting, J. A. (2005), Computational statistics. Wiley-Interscience, Hoboken, NJ
133. Goodman, L. A. (1974), Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215-31
134. Goudie, I. B. J. and Goudie, M. (2007), Who captures the marks for the Petersen estimator? Journal of the Royal Statistical Society, Series A 170, 825-839
135. Gramacy, R. B., Samworth, R. J. and King, R. (2009), Importance tempering. Statistics and Computing - in press
136. Green, P. and Silverman, B. (1994), Nonparametric regression and generalized linear models. Chapman & Hall, New York
137. Green, P. J. (1995), Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711-732
138. Green, P. J. and Mira, A. (2001), Delayed rejection in reversible jump Metropolis-Hastings. Biometrika 88, 1035-53
139. Hammond, P. S., Mizroch, S. A. and Donovan, G. P. (1990), Individual recognition of cetaceans: Use of photo-identification and other techniques to estimate population size. Technical report, International Whaling Commission, Special issue 12
140. Heisey, D. M. and Nordheim, E. V. (1990), Biases in the Pollock and Cornelius method of estimating nest survival. Biometrics 46, 855-862
141. Heisey, D. M. and Nordheim, E. V. (1995), Modelling age-specific survival in nesting studies, using a general approach for doubly-censored and truncated data. Biometrics 51, 51-60
142. Hestbeck, J. B., Nichols, J. D. and Malecki, R. A. (1991), Estimates of movement and site fidelity using mark-resight data of wintering Canada geese. Ecology 72, 523-533
143. Hinde, A. (1998), Demographic methods. Arnold, London
144. Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T. (1999), Bayesian model averaging: A tutorial. Statistical Science 14, 382-401
145. Huggins, R. H. (1991), Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47, 725-732
146. Jamieson, L. and Brooks, S. (2004a), Density dependence in North American ducks. Animal Biodiversity and Conservation 27, 113-128
147. Jamieson, L. and Brooks, S. P. (2004b), Assessing density and covariate dependence: a Bayesian assessment of North American ducks. Technical report, University of Cambridge
148. Jennison, C. (1993), Discussion on the meeting on the Gibbs sampler and other Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B 55, 54-56
149. Johnson, D. S. and Hoeting, J. A. (2003), Autoregressive models for capture-recapture data: A Bayesian approach. Biometrics 59, 341-350
150. Jonsen, I. D., Flemming, J. M. and Myers, R. A. (2005), Robust state-space modelling of animal movement data. Ecology 86, 2874-2880
151. Kalman, R. R. (1960), A new approach to linear filtering and prediction problems. Transactions of ASME Journal of Basic Engineering 82, 35-45
152. Kanyamibwa, S., Schierer, A., Pradel, R. and Lebreton, J. D. (1990), Changes in adult survival rates in a western European population of the White Stork ciconia ciconia. Ibis 132, 127-135
153. Karunamuni, R. and Quinn, T. (1995), Bayesian estimation of animal abundance for the line transects sampling. Biometrics 51, 1325-1337
154. Kass, R. E. and Raftery, A. E. (1995), Bayes factors. Journal of the American Statistical Association 90, 773-793
155. Kennedy, M. (ed.) (1990), Australia’s endangered species: The extinction dilemma. Simon and Schuster, Brookvale, New South Wales
156. Kerman, J. and Gelman, A. (2006), Tools for Bayesian data analysis in R. Statistical Computing and Graphics 17, 9-13
157. Kéry, M. and Royle, J. A. (2007), Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology 45, 589-598
158. King, R. (2010), Statistical ecology. In S. P. Brooks, X. Meng, A. Gelman and G. Jones (eds.), Handbook of Markov chain Monte Carlo; Methods and Applications, Chapman & Hall, London
159. King, R., Bird, S. M., Brooks, S. P., Hutchinson, S. J. and Hay, G. (2005), Prior information in behavioural capture-recapture methods: Demography influences injectors’ propensity to be listed on data sources and their drugs-related mortality. American Journal of Epidemiology 162, 1-10
160. King, R. and Brooks, S. P. (2002a), Bayesian model discrimination for multiple strata capture-recapture data. Biometrika 89, 785-806
161. King, R. and Brooks, S. P. (2002b), Model selection for integrated recovery/recapture data. Biometrics 58, 841-851
162. King, R. and Brooks, S. P. (2003a), A note on closed form likelihoods for Arnason-Schwarz models. Biometrika 90(2), 435-444
163. King, R. and Brooks, S. P. (2003b), Survival and spatial fidelity of mouon: The effect of location, age and sex. Journal of Agricultural, Biological, and Environmental Statistics 8, 486-531
164. King, R. and Brooks, S. P. (2004a), Bayesian analysis of the survival of Hector’s dolphins. Animal Biodiversity and Conservation 27, 343-354
165. King, R. and Brooks, S. P. (2004b), A classical study of catch-effort models for Hector’s dolphins. Journal of the American Statistical Society 99, 325-333
166. King, R. and Brooks, S. P. (2008), On the Bayesian estimation of a closed population size in the presence of heterogeneity and model uncertainty. Biometrics 64, 816-824
167. King, R., Brooks, S. P. and Coulson, T. (2008a), Analysing complex capture-recapture data in the presence of individual and temporal covariates and model uncertainty. Biometrics 64, 1187-1195
168. King, R., Brooks, S. P., Mazzetta, C., Freeman, S. N. and Morgan, B. J. T. (2008b), Identifying and diagnosing population declines: A Bayesian assessment of lapwings in the UK. Journal of the Royal Statistical Society, Series C 57, 609-632
169. King, R., Brooks, S. P., Morgan, B. J. T. and Coulson, T. (2006), Bayesian analysis of Soay sheep survival. Biometrics 62, 211-220
170. Kotz, S. and Johnson, N. L. (eds.) (1992), Breakthroughs in Statistics. Springer Verlag, New York
171. Lang, S. and Brezger, A. (2004), Bayesian P-splines. Journal of Computational and Graphical Statistics 13, 183-212
172. Laplace, P. S. (1786), Sur les naissances, les mariages et les morts. Histaire de I’Academie Royale des Sciences, 1783, Paris, 693-702
173. Lebreton, J. D., Burnham, K. P., Clobert, J. and Anderson, D. R. (1992), Modelling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecological Monographs 62, 67-118
174. Lee, P. M. (2004), Bayesian statistics: An introduction. Arnold, London, third edition
175. Lefkovitch, L. P. (1965), The study of population growth in organisms grouped by stages. Biometrics 21, 1-18
176. Leslie, P. H. (1945), On the use of matrices in certain population mathematics. Biometrika 33, 183-212
177. Leslie, P. H. (1948), Some further notes on the use of matrices in certain population mathematics. Biometrika 35, 213-245
178. Lindley, D. V. (1965), Introduction to probability and statistics from a Bayesian viewpoint. Cambridge University Press, Cambridge
179. Link, W. A. (2003), Nonidentifiability of population size from capturerecapture data with heterogeneous detection probabilities. Biometrics 59, 1123-1130
180. Link, W. A. and Barker, R. J. (2005), Modelling association among demographic parameters in analysis of open population capture-recapture data. Biometrics 61, 46-54
181. Link, W. A. and Barker, R. J. (2008), Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly- Seber model. Environmental and Ecological Statistics 15, 79-87
182. Link, W. A. and Barker, R. J. (2009), Bayesian Inference with ecological applications. Academic Press
183. Link, W. A., Cam, E., Nichols, J. D. and Cooch, E. G. (2002), Of BUGS and birds: Markov chain Monte Carlo for hierarchical modelling in wildlife research. Journal of Wildlife Management 66, 277-291
184. Liu, J. S. (2001), Monte Carlo strategies in scientific computing. Springer
185. Loison, A., Festa-Bianchet, M., Gaillard, J. M., Jorgenson, J. T. and Jullien, J. M. (1999), Age-specific survival in five populations of ungulates: Evidence of senescence. Ecology 80, 2539-2554
186. Lunn, D. J., Best, N. and Whittaker, J. (2009), Generic reversible jump MCMC using graphical models. Statistics and Computing - in press
187. Lunn, D. J., Thomas, A., Best, N., and Spiegelhalter, D. (2000), WinBUGS - a Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325-337
188. Lunn, D. J., Whittaker, J. C. and Best, N. (2006), A Bayesian toolkit for genetic association studies. Genetic Epidemiology 30, 231-247
189. Madigan, D. and Raftery, A. E. (1994), Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association 89, 1535-1547
190. Marin, J. and Robert, C. R. (2007), Bayesian core: A practical approach to computational Bayesian statistics. Springer, New York
191. Marinari, E. and Parisi, G. (1992), Simulated tempering: A new Monte Carlo scheme. Europhysics Letters 19, 451-458
192. Massot, M., Clobert, J., Pilorge, T., Lecomte, J. and Barbault, R. (1992), Density dependence in the common lizard: Demographic consequences of a density manipulation. Ecology 73, 1742-1756
193. McAllister, M. K. and Kirkwood, G. P. (1998a), Bayesian stock assessment: A review and example application using the logistic model. ICES Journal of Marine Science 55, 1031-1060
194. McAllister, M. K. and Kirkwood, G. P. (1998b), Using Bayesian decision analysis to help achieve a precautoinary approach for managing dolphin fisheries. Canadian Journal of Fisheries and Aquatic Science 55, 2642-2661
195. McCarthy, M. A. (2007), Bayesian methods for ecology. Cambridge University Press, Cambridge
196. McCrea, R. S. and Morgan, B. J. T. (2009), Multi-site mark recapture model selection using score tests – in revision for Biometrics
197. Millar, R. B. (2004), Sensitivity of Bayes estimators to hyper-parameters with an application to maximum yield from fisheries. Biometrics 60, 536-542
198. Millar, R. B. and Meyer, R. (2000), Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Journal of the Royal Statistical Society, Series C 49, 327-342
199. Mingoti, A. S. (1999), Bayesian estimator for the total number of distinct species when quadrat sampling is used. Journal of Applied Statistics 26, 469-483
200. Morgan, B. J. T. (1984), Elements of simulation. Chapman & Hall, London
201. Morgan, B. J. T. (1992), Analysis of quantal response data. Chapman & Hall, London
202. Morgan, B. J. T. (2008), Applied stochastic modelling. Chapman & Hall, London, second edition
203. Morgan, B. J. T., Lebreton, J. D. and Freeman, S. N. (1997), Ornithology, Statistics in. Encyclopedia of Statistical Sciences, Update Volume 1, 438-447
204. Morgan, B. J. T., Palmer, K. J. and Ridout, M. S. (2007a), Negative score test statistic. The American Statistician 61, 285-288
205. Morgan, B. J. T., Revell, D. J. and Freeman, S. N. (2007b), A note on simplifying likelihoods for site occupancy models. Biometrics 63, 618-621
206. Morgan, B. J. T. and Ridout, M. S. (2008), A new mixture model for recapture heterogeneity. Journal of the Royal Statistical Society, Series C 57, 433-446
207. Morgan, B. J. T. and Thomson, D. L. (eds.) (2002), Statistical Analysis of data from marked bird populations: special issue of Journal of Applied Statistics, Vol 29, Nos 1-4. Taylor and Francis
208. Morgan, B. J. T. and Viallefont, A. (2002), Ornithological data. Encyclopedia of Environmetrics 3, 1495-1499
209. Moyes, K. (2007), Demographic Consequences of Individual Variation. Ph.D. thesis, University of Kent
210. Neal, R. (2003), Slice sampling (with discussion). Annals of Statistics 31, 705-767
211. Neal, R. M. (1996), Sampling from multimodal distributions using tempered transition. Statistics and Computing 6, 353-366
212. Neal, R. N. (2001), Annealed importance sampling. Statistics and Computing 11, 125-129
213. Newman, K. B. (1998), State space modelling of animal movement and mortality with application to salmon. Biometrics 54, 1290-1314
214. Newman, K. B. (2000), Hierarchical modeling of salmon harvest and migration. Journal of Agricultural, Biological, and Environmental Modelling 5, 430-455
215. Newman, K. B. (2003), Modelling paired release-recovery data in the presence of survival and capture heterogeneity with application to marked juvenile salmon. Statistical Modelling 3, 157-177
216. Newman, K. N., Buckland, S. T., Lindley, S. T., Thomas, L. and Fernández, C. (2006), Hidden process models for animal population dynamics. Ecological Applications 16, 74-86
217. Newman, K. N., Fernández, C., Thomas, L. and Buckland, S. T. (2009), Monte carlo inference for state-space models of wild animal populations. Biometrics 65, 572-583
218. Ngo, L. and Wand, M. (2004), Smoothing with mixed model software. Journal of Statistical Software 9, 1-54
219. Nichols, J., Hines, J. and Blums, P. (1997), Tests for sensecent decline in annual survival probabilities of common pochards, Aythya ferina. Ecology 78(4), 1009-1018
220. Nichols, J. D., Sauer, J. R., Pollock, K. H. and Hestbeck, J. B. (1992), Estimating transition probabilities for stage-based population projection matrices using capture-recapture data. Ecology 73, 306-312
221. North, P. M. and Morgan, B. J. T. (1979), Modelling heron survival using weather data. Biometrics 35, 667-681
222. O’Hagan, A. O. (1998), Eliciting expert beliefs in substantial practical applications. The Statistician 47, 21-36
223. O’Hara, R. B., Arjas, E., Toivonen, H. and Hanski, I. (2002), Bayesian analysis of metapopulation data. Ecology 83, 2408-2415
224. O’Hara, R. B., Lampila, S. and Orell, M. (2009), Estimation of rates of births, deaths, and immigration from mark-recapture data. Biometrics 65
225. O’Hara, R. B. and Silanpaa, M. J. (2009), A review of Bayesian variable selection methods, what, how and which. Bayesian Analysis 4, 85-118
226. Otis, D. L., Burnham, K. P., White, G. C. and Anderson, D. R. (1978), Statistical inference from capture data on closed animal populations. Wildlife Monographs 62, 1-135
227. Peach, W. J., Baillie, S. R. and Balmer, D. E. (1998), Long-term changes in the abundance of passerines in Britain and Ireland as measured by constant effort mist-netting. Bird Study 45, 257-275
228. Peskun, P. H. (1973), Optimum Monte Carlo sampling using Markov chains. Biometrika 60, 607-612
229. Pledger, S. (2000), Unified maximum likelihood estimates for closed capture-recapture models using mixtures. Biometrics 56, 434-442
230. Pledger, S., Pollock, K. H. and Norris, J. L. (2003), Open capture-recapture models with heterogeneity: I. Cormack-Jolly-Seber model. Biometrics 59, 786-794
231. Polacheck, T., Hilborn, R. and Punt, A. E. (1993), Fitting surplus production models: Comparing methods and measuring uncertainty. Canadian Journal of Fisheries and Aquatic Sciences 50, 2597-2607
232. Pollock, K. (1991), Modeling capture, recapture and removal statistics for estimation of demographic parameters for fish and wildlife populations: Past, present and future. Journal of the American Statistical Association 86, 226-238
233. Pollock, K. (2000), Capture-recapture models. Journal of the American Statistical Association 95, 293-296
234. Pollock, K. H., Bunck, C. M., Winterstein, S. R. and Chen, C. L. (1995), A capture-recapture survival analysis model for radio-tagged animals. Journal of Applied Statistics 22, 661-672
235. Pollock, K. H. and Cornelius, W. L. (1988), A distribution-free nest survival model. Biometrics 44, 397-404
236. Pollock, K. H., Nichols, J. D., Brownie, C. and Hines, J. E. (1990), Statistical inference for capture-recapture experiments. Wildlife Monographs 107, 1-97
237. Pradel, R. (1996), Utilization of capture-mark-recapture for the study of recruitment and population growth rate. Biometrics 52, 703-709
238. Pradel, R. (2005), Multievent: An extension of multistate capture-recapture models to uncertain states. Biometrics 61, 442-447
239. Propp, J. G. and Wilson, D. B. (1996), Exact sampling with coupled Markov chain and applications to statistical mechanics. Random Structure and Algorithms 9, 223-252
240. Punt, A. E. and Hilborn, R. (1997), Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries 7, 35-63
241. Raftery, A. E. and Lewis, S. M. (1996), Implementing MCMC. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (eds.), Markov chain Monte Carlo in practice, Chapman & Hall, London, pp. 115-130
242. Reddingius, J. (1971), Gambling for existence: A discussion of some theoretical problems in animal population ecology. Acta Biotheoretica 20, 1-208
243. Reiersøl, O. (1950), Identifiability of a linear relation between variables which are subject to error. Econometrica 18, 375-389
244. Reynolds, T., King, R., Harwood, J., Frederiksen, M., Wanless, S. and Harris, M. (2009), Integrated data analyses in the presence of emigration and tag-loss. Journal of Agricultural, Biological, and Environmental Statistics - in press
245. Richardson, S. and Green, P. J. (1997), On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society, Series B 59, 731-792
246. Ripley, B. D. (1987), Stochastic simulation. Wiley, Chichester
247. Rivot, E. and Prévost, E. (2002), Hierarchical Bayesian analysis of capturemark- recapture data. Canadian Journal of Fisheries and Aquatatic Sciences 59, 1768-1784
248. Rivot, E., Prévost, E. and Parent, E. (2001), How robust are Bayesian posterior inferences based on a Ricker model with regards to measurement errors and prior assumptions about parameters? Canadian Journal of Fisheries and Aquatatic Sciences 58, 2284-2297
249. Rivot, E., Prévost, E., Parent, E. and Bagliniére, J. L. (2004), A Bayesian state-space modelling framework for fitting a salmon stage-structured population dynamics model to multiple time series of field data. Ecological Modelling 179, 463-485
250. Rizzo, M. L. (2007), Statistical computing with R. Chapman & Hall, London
251. Robert, C. P. (1994), The Bayesian choice: A decision-theoretic motivation. Springer, New York
252. Robert, C. P. (2007), The Bayesian choice: From decision-theoretic foundations to computational implementation. Springer, New York, second edition
253. Robert, C. R. and Casella, G. (2004), Monte Carlo statistical methods. Springer, New York, second edition
254. Roberts, G. O. (1996), Markov chain concepts related to sampling algorithms. In W. R. Gilks, S. Richardson and D. J. Spiegelhalter (eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, pp. 45-58
255. Roberts, G. O. and Sahu, S. K. (1997), Updating schemes, covariance structure, blocking and parameterisation for the Gibbs sampler. Journal of the Royal Statistical Society, Series B 59, 291-318
256. Rothenberg, T. J. (1971), Identification in parametric models. Econometrica 39, 577-591
257. Royle, J. A. (2006), Site occupancy models with heterogeneous detection probabilities. Biometrics 62, 97-102
258. Royle, J. A. (2008), Modeling individual effects in the Cormack-Jolly-Seber model: A state-space formulation. Biometrics 64, 364-370
259. Royle, J. A. and Dorazio, R. M. (2008), Hierarchical modeling and inference in ecology. Academic Press
260. Royle, J. A., Dorazio, R. M. and Link, W. A. (2007), Analysis of multinomial models with unknown index using data augmentation. Journal of Computational and Graphical Statistics 16, 67-85
261. Royle, J. A. and Kéry, M. (2007), A Bayesian state-space formulation of dynamic occupancy models. Ecology 88, 1813-1823
262. Rue, H. (2001), Fast sampling of Gaussian Markov random fields. Journal of the Royal Statistical Society, Series B 63, 325-338
263. Ruppert, D. (2002), Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics 11, 735-757
264. Ruppert, D., Wand, M. P. and Carroll, R. (2003), Semiparametric regression. Cambridge University Press, Cambridge
265. Savage, L. J. (1954), The Foundations of Statistics. Wiley, New York
266. Schaefer, M. B. (1954), Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Bulletin of the Inter-American Tropical Tuna Commission 1, 25-26
267. Schaub, M., Kania, W. and Koppen, U. (2005), Variation of primary production during winter induces synchrony in survival rates in migratory white storks Ciconi ciconia. Journal of Animal Ecology 74, 656-666
268. Schwarz, C. G., Schweigert, J. F. and Arnason, A. N. (1993), Estimating migration rates using tag-recovery data. Biometrics 49, 177-193
269. Schwarz, C. J. and Seber, G. A. F. (1999), A review of estimating animal abundance III. Statistical Science 14, 427-456
270. Seber, G. A. F. (1971), Estimating age-specific rates from bird-bands when the reporting rate is constant. Biometrika 58, 491-497
271. Seber, G. A. F. (1982), The estimation of animal abundance and related parameters. MacMillan, New York
272. Senar, J., Dhondt, A. and Conroy, M. E. (2004), The quantitative study of marked individuals in ecology, evolution and conservation biology: A foreword to the EURING 2003 conference. Animal Biodiversity and Conservation 27, 1-2
273. Skalski, J. R., Robson, D. S. and Simmons, M. A. (1983), Comparative census procedures using single mark-recapture methods. Ecology 665, 1006-1015
274. Smith, A. F. M. and Gelfand, A. E. (1992), Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician 46, 84-88
275. Smith, P. (1991), Bayesian analyses for a multiple capture-recapture model. Biometrics 78, 399-407
276. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002), Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-616
277. Spiegelhalter, D. J., Thomas, A. and Best, N. G. (1996), Computation on Bayesian graphical models. In J. M. Bernardo, A. F. M. Smith, A. P. Dawid and J. O. Berger (eds.), Bayesian statistics 5, Oxford University Press, Oxford, pp. 407-425
278. Stanghellini, E. and van der Heijden, P. G. M. (2004), A multiple-record systems estimation methods that takes observed and unobserved heterogeneity into account. Biometrics 60, 510-516
279. Stephens, M. (2000), Bayesian analysis of mixture models with an unknown number of components - an alternative to reversible jump methods. Annals of Statistics 28, 40-74
280. Stigler, S. M. (1986), The History of Statistics: The Measurement of Un- certainty before 1900. Belknap Press, Cambridge, MA
281. Sturtz, S., Ligges, U. and Gelman, A. (2005), R2WinBUGS: A package for running WinBUGS from R. Joural of Statistical Software 12, 1-16
282. Tardella, L. (2002), A new Bayesian method for nonparametric capturerecapture models in presence of heterogeneity. Biometrika 89, 807-817
283. ter Braak, C. J. F. and Etienne, R. S. (2003), Improved Bayesian analysis of metapopulation data with an application to a tree frog metapopulation. Ecology 84, 231-241
284. Thomas, L., Buckland, S. T., Newman, K. B. and Harwood, J. (2005), A unified framework for modelling wildlife population dynamics. Australian and New Zealand Journal of Statistics 47, 19-34
285. Thomas, L., Laake, J. L., Rexstad, E., Strindberg, S., Marques, F. F. C., Buckland, S. T., Borchers, D. L., Anderson, D. R., Burnham, K. P., Burt, M. L., Hedley, S. L., Pollard, J. H., Bishop, J. R. B. and Marques, T. (2009), Distance 6.0 Release 2. University of St. Andrews, Research Unit for Wildlife Population Assessment, University of St. Andrews, UK. http://www.ruwpa.st-and.ac.uk/distance
286. Thurstone, L. L. (1947), Multiple-factor analysis. University of Chicago Press, Chicago
287. Viallefont, A., Lebreton, J.-D., Reboulet, A.-M. and Gory, G. (1998), Parametric identifiability and model selection in capture-recapture models: A numerical approach. Biometrical Journal 40, 313-325
288. Vounatsou, P. and Smith, A. F. M. (1995), Bayesian analysis of ringrecovery data via Markov chain Monte Carlo simulation. Biometrics 51, 687-708
289. West, M. and Harrison, J. (1997), Bayesian forecasting and dynamic models. Springer-Verlag, New York
290. White, G. C. and Burnham, K. P. (1999), Program MARK: Survival estimation from populations of marked animals. Bird Study 46 suppl, 120-39
291. Wilby, R. L., O’Hare, G. and Barnsley, N. (1997), The North Atlantic Oscillation and British Isles climate variability, 1865-1996. Weather 52, 266-276
292. Williams, B. K., Nichols, J. D. and Conroy, M. J. (2002), Analysis and management of animal populations. Academic Press, San Diego, CA
293. Yates, F. (1937), The design and analysis of factorial experiments. Technical Report 35, Imperial Bureau of Soil Science, Harpenden, England
294. Zheng, C., Ovaskainen, O., Saastamoinen, M. and Hanski, I. (2007), Age-dependent survival analyzed with Bayesian models of mark-recapture data. Ecology 88, 1970-1976