Accounting for Demographic and Environmental Stochasticity, Observation Error, and Parameter Uncertainty in Fish Population Dynamics Models

References

• Adkison , M. D. and Peterman , R. M. 2000 . Predictability of Bristol Bay, Alaska, sockeye salmon returns one to four years in the future . North American Journal of Fisheries Management , 20 : 69 – 80 .
   Google Scholar
• Barrowman , N. J. , Myers , R. A. , Hilborn , R. , Kehler , D. G. and Field , C. A. 2003 . The variability among populations of coho salmon in the maximum reproductive rate and depensation . Ecological Applications , 13 : 784 – 793 .
   Web of Science ®Google Scholar
• 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 .
   PubMed Web of Science ®Google Scholar
• Buckland , S. T. , Newman , K. B. , Thomas , L. and Koesters , N. B. 2004 . State-space models for the dynamics of wild animal populations . Ecological Modelling , 171 : 157 – 175 .
   Web of Science ®Google Scholar
• Burnham , K. P. and Anderson , D. R. 1998 . Model selection and inference: a practical information-theoretic approach New York : Springer-Verlag .
   Google Scholar
• Calder , C. , Lavine , M. , Müller , P. and Clark , J. S. 2003 . Incorporating multiple sources of stochasticity into population dynamics models . Ecology , 84 : 1395 – 1402 .
   Web of Science ®Google Scholar
• Caswell , H. 2001 . Matrix population models, , 2nd edition Sunderland , Massachusetts : Sinauer Associates, Inc. .
   Google Scholar
• Clark , J. S. and Bjornstad , O. N. 2004 . Population time series: process variability, observation errors, missing variables, lags, and hidden states . Ecology , 85 : 3140 – 3150 .
   Web of Science ®Google Scholar
• Devroye , L. 1986 . Nonuniform random variate generation New York : Springer-Verlag .
   Google Scholar
• Doucet , A. , de Freitas , N. and Gordon , N. 2001 . “ An introduction to sequential Monte Carlo methods ” . In Sequential Monte Carlo methods in practice Edited by: Doucet , A. and Gordon , N. 3 – 14 . Berlin : Springer-Verlag .
   Google Scholar
• Gaines , P. D. and Poytress , W. R. 2004 . “ Brood-year 2003 winter Chinook production indices with comparisons to adult returns ” . In Report prepared for the California Bay−Delta Authority, Annual Report 2003, Project ERP-01-N44 U.S. Fish and Wildlife Service. Sacramento. Available: http://www.fws.gov/pacific/redbluff/PDF/ERP-01-N44 Annual report (2003).pdf. (September 2005.)
   Google Scholar
• Green , P. J. 1995 . Reversible Jump MCMC computation and model determination . Biometrika , 82 : 711 – 732 .
   Web of Science ®Google Scholar
• Harvey , A. 1989 . Forecasting, structural time series models, and the Kalman filter Cambridge , , UK : Cambridge University Press .
   Google Scholar
• Healey , M. C. 1991 . “ Life history of Chinook salmon (Oncorhynchus tshawytscha) ” . In Pacific salmon life histories Edited by: Groot , C. and Margolis , L. 311 – 393 . Vancouver : UBC Press .
   Google Scholar
• Hoeting , J. , Madigan , D. , Raftery , A. E. and Volinsky , C. 1999 . Bayesian model averaging . Statistical Science , 14 : 382 – 401 .
   Web of Science ®Google Scholar
• Kass , R. E. and Raftery , A. E. 1995 . Bayes factors . Journal of the American Statistical Association , 90 : 773 – 795 .
   Web of Science ®Google Scholar
• Killam , D. and Harvey-Arrison , C. 2005 . “ Chinook salmon spawner populations for the upper Sacramento River basin, 2003 ” . In Technical Report 04–3 Redding : California Department of Fish and Game, Sacramento River Salmon and Steelhead Assessment Project .
   Google Scholar
• Liu , J. and West , M. 2001 . “ Combined parameter and state estimation in simulation-based filtering ” . In Sequential Monte Carlo methods in practice Edited by: Doucet , A. and Gordon , N. 197 – 223 . Berlin : Springer-Verlag .
   Google Scholar
• Liu , J. S. 2001 . Monte Carlo strategies in scientific computing New York : Springer .
   Google Scholar
• Liu , J. S. and Chen , R. 1998 . Sequential Monte Carlo methods for dynamic systems . Journal of the American Statistical Association , 93 : 1032 – 1044 .
   Web of Science ®Google Scholar
• Liu , J. S. , Chen , R. and Logvinenko , T. 2001 . “ A theoretical framework for sequential importance sampling with resampling ” . In Sequential Monte Carlo methods in practice Edited by: Doucet , A. and Gordon , N. 225 – 246 . Berlin : Springer-Verlag .
   Google Scholar
• MacDonald , P. D. M. , Smith , H. D. and Jantz , L. 1987 . The utility of Babine smolt enumerations in management of Babine and other Skeena River sockeye salmon (Oncorhynchus nerka) stocks . Canadian Special Publication of Fisheries and Aquatic Science , 96 : 280 – 295 .
   Google Scholar
• Martin , C. D. , Gaines , P. D. and Johnson , R. R. 2001 . “ Estimating the abundance of Sacramento River juvenile winter Chinook salmon with comparisons to adult escapement ” . In Final Report, Red Bluff Research Pumping Plant Report Series Vol. 5 , California : Red Bluff . U.S. Fish and Wildlife Service
   Google Scholar
• Mendelssohn , R. 1988 . Some problems in estimating sizes from catch-at-age data . Fishery Bulletin , 86 : 617 – 630 .
   Web of Science ®Google Scholar
• Meyer , R. and Millar , R. B. 1999 . Bayesian stock assessment using a state-space implementation of the delay difference model . Canadian Journal of Fisheries and Aquatic Sciences , 56 : 37 – 52 .
   Web of Science ®Google Scholar
• Millar , R. B. and Meyer , R. 2000 . Nonlinear state-space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling . Applied Statistics , 49 : 327 – 342 .
   Web of Science ®Google Scholar
• Newman , K. B. 1998 . State-space modeling of animal movement and mortality with application to salmon . Biometrics , 54 : 1290 – 1314 .
   Web of Science ®Google Scholar
• Newman , K. B. 2000 . Hierarchic modeling of salmon harvest and migration . Journal of Agricultural, Biological, and Environmental Statistics , 5 : 430 – 455 .
   Web of Science ®Google Scholar
• 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 .
   Web of Science ®Google Scholar
• Newman , K. B. , Buckland , S. T. , Lindley , S. T. , Thomas , L. and Fernández , C. 2006 . Hidden process models for animal population dynamics . Ecological Applications , 16 : 74 – 86 .
   PubMed Web of Science ®Google Scholar
• NMFS (National Marine Fisheries Service). 1997 . NMFS proposed recovery plan for the Sacramento River winter-run Chinook Long Beach , California : NMFS, Southwest Regional Office .
   Google Scholar
• Peterman , R. M. 1982 . Model of salmon age structure and its use in preseason forecasting and studies of marine survival . Canadian Journal of Fisheries and Aquatic Sciences , 39 : 1444 – 1452 .
   Web of Science ®Google Scholar
• Press , W. H. , Teukolsky , S. A. , Vetterling , W. T. and Flannery , B. P. 1992 . Numerical recipes in C, , 2nd edition New York : Cambridge University Press .
   Google Scholar
• Ripley , B. 1987 . Stochastic simulation New York : Wiley and Sons .
   Google Scholar
• Rivot , E. , Prévost , E. , Parent , E. and Baglinière , J. L. 2004 . A Bayesian state-space modelling framework for fitting a salmon stage-structured population model to multiple time series of field data . Ecological Modelling , 179 : 463 – 485 .
   Web of Science ®Google Scholar
• Rubin , D. B. 1988 . “ Using the SIR algorithm to simulate posterior distributions ” . In Bayesian statistics 3 Edited by: Bernardo , J. M. , DeGroot , M. H. , Lindley , D. V. and Smith , A. F. M. 395 – 402 . Oxford , , UK : Oxford University Press .
   Google Scholar
• Schnute , J. 1994 . A general framework for developing sequential fisheries models . Canadian Journal of Fisheries and Aquatic Sciences , 51 : 1676 – 1688 .
   Web of Science ®Google Scholar
• Schnute , J. and Kronlund , A. R. 2002 . Estimating salmon stock−recruitment relationships from catch and escapement data . Canadian Journal of Fisheries and Aquatic Sciences , 59 : 433 – 449 .
   Web of Science ®Google Scholar
• Snider , W. and Titus , R. G. 2000 . “ Timing, composition, and abundance of juvenile anadromous salmonid emigration in the Sacramento River near Knights Landing, October 1998−September 1999 ” . In Stream Evaluation Program Technical Report 00-6 Sacramento California Department of Fish and Game
   Google Scholar
• Speed , T. 1993 . “ Modelling and managing a salmon population ” . In Statistics for the environment Edited by: Barnett , V. and Turkman , K. F. 267 – 292 . New York : Wiley and Sons .
   Google Scholar
• Spiegelhalter , D. J. , Best , N. G. , Carlin , B. P. and van der Linde , A. 2003a . Bayesian measures of model complexity and fit . Journal of the Royal Statistical Society B , 64 ( 4 ) : 583 – 616 .
   Google Scholar
• Spiegelhalter , D. J. , Thomas , A. and Best , N. G. 2003b . WinBUGS, version 1.4, user manual Medical Research Council, Cambridge, UK, and Imperial College of Science, Technology, and Medicine, London. Available: http://www.mrc-bsu-cam.ac.uk/bugs. (September 2005.)
   Google Scholar
• Sullivan , P. 1992 . A Kalman filter approach to catch-at-length-analysis . Biometrics , 48 : 237 – 257 .
   Web of Science ®Google Scholar
• 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 .
   Web of Science ®Google Scholar