Signal extraction from long-term ecological data using Bayesian and non-Bayesian state-space models

Ecological Informatics

Volumn 4, Issue 2, 2009, Pages 69-75

  Wang, Guiming ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Bootstrapped state space models; Ecological information; Gibbs algorithm; Metropolis Hastings algorithm; Multiple try Metropolis algorithm; System dynamics

Indexed keywords

ALGORITHM; BAYESIAN ANALYSIS; BOOTSTRAPPING; COMPARATIVE STUDY; ECOLOGICAL MODELING; KALMAN FILTER; MARKOV CHAIN; MONTE CARLO ANALYSIS; RECONSTRUCTION; TIME SERIES;

ANIMALIA;

EID: 67349245859     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2009.01.005     Document Type: Review

References (74)

1. Besbeas P., and Freeman S.N. Methods for joint inference from panel survey and demographic data. Ecology 87 (2006) 1138-1145
2. Besbeas P., Freeman S.N., Morgan B.J.T., and Catchpole E.A. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58 (2002) 540-547
3. Besbeas P., Freeman S.N., and Morgan B.J.T. The potential of integrated population modelling. Australian & New Zealand Journal of Statistics 47 (2005) 35-48
4. Best N.G., Cowles M.K., and Vines S.K. CODA Manual version 0.30. MRC Biostatistics Unit (1995), Cambridge University, Cambridge, U.K
5. Brockwell P.J., and Davis R.A. Introduction to Time Series and Forecasting (2002), Springer, New York, New York
6. Buckland S.T., Newman K.B., Thomas L., and Koesters N.B. State-space models for the dynamics of wild animal populations. Ecological Modelling 171 (2004) 157-175
7. Buckland S.T., Newman K.B., Fernandez C., Thomas L., and Harwood J. Embedding population dynamics models in inference. Statistical Science 22 (2007) 44-58
8. Burnham K.P., and Anderson D.R. Model Selection and Inference: A Practical Information-theoretic Approach (2002), Springer-Verlag, New York, New York, USA
9. Calder C., Lavine M., Muller P., and Clark J.S. Incorporating multiple sources of stochasticity into dynamic population models. Ecology 84 (2003) 1395-1402
10. Carroll R.J., Ruppert D., and Stefanski L.A. Measurement Error in Nonlinear Models (1995), Chapman & Hall/CRC, Boca Raton, Florida, USA
11. Clark J.S., and Bjornstad C.N. Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology 85 (2004) 3140-3150
12. Coughenour M.B., and Singer F.J. Elk population processes in Yellowstone National Park under the policy of natural regulation. Ecological Applications 6 (1996) 573-593
13. De Valpine P., and Hastings A. Fitting population models incorporating process noise and observation error. Ecological Monographs 72 (2002) 57-76
14. Dennis B., Ponciano J.M., Lele S.R., Taper M.L., and Staples D.F. Estimating density dependence, process noise, and observation error. Ecological Monographs 76 (2006) 323-341
15. Dowd M. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo. Journal of Marine Systems 68 (2007) 439-456
16. Durbin J., and Koopman S.J. Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. Journal of the Royal Statistical Society Series B-Statistical Methodology 62 (2000) 3-29
17. Durbin J., and Koopman S.J. Time Series Analysis by State Space Models (2001), Oxford University Press, Oxford, U.K
18. Forester J.D., Ives A.R., Turner M.G., Anderson D.P., Fortin D., Beyer H.L., Smith D.W., and Boyce M.S. State-space models link elk movement patterns to landscape characteristics in Yellowstone National Park. Ecological Monographs 77 (2007) 285-299
19. Fryxell J.M., Wilmshurst J.F., and Sinclair A.R.E. Predictive models of movement by Serengeti grazers. Ecology 85 (2004) 2429-2435
20. Fuller W.A. Measurement Error Models (1987), John Wiley & Sons, New York, New York, USA
21. Gaucherel C., Campillo F., Misson L., Guiot J., and Boreux J.-J. Parameterization of a process-based tree-growth model: comparison of optimization, MCMC and particle filtering algorithms. Environmental Modelling and Software 23 (2008) 1280-1288
22. Gauthier G., Besbeas P., Lebreton J.D., and Morgan B.J.T. Population growth in snow geese: a modeling approach integrating demographic and survey information. Ecology 88 (2007) 1420-1429
23. Gelb A., Kasper J.F., Nash R.A., Price C.F., and Sutherland A.A. Applied Optimal Estimation (1974), MIT press, Cambridge, Massachusetts, USA
24. Geweke J. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Bernardo J.M., Berger J.O., D. A.P., and S. A.F.M. (Eds). Bayesian Statistics 4 (1992), Clarendon Press, Oxford, U.K. 169-193
25. Geweke J., and Tanizaki H. Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling. Computational Statistics & Data Analysis 37 (2001) 151-170
26. Gimenez O., Rossi V., Choquet R., Dehais C., Doris B., Varella H., Vila J.P., and Pradel R. State-space modelling of data on marked individuals. Ecological Modelling 206 (2007) 431-438
27. Giminez O., Bonner S., King P., Parker R.A., Brooks S., Jamieson L.E., Grosbois V., Morgan B.J.T., and Thomas L. WinBUGS for population ecologists: Bayesian modeling using Markov chain Monte Carlo methods. In: Thomson D.L., Cooch E.G., and Conroy M.J. (Eds). Environmental and Ecological Statistics Springer (2009), New York, USA 885-918
28. Givens G.H., and Hoeting J.A. Computational Statistics (2005), John Wiley & Sons, Hoboken, New Jersey, USA
29. Gompertz B. On the nature of the function expressive of the law of human mortality, and on the new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London B Biological Sciences 115 (1825) 513-585
30. Grewal M.S., and Andrews A.P. Kalman Filtering: Theory and Practice using MATLAB (2001), John Wiley & Sons, New York, New York, USA
31. Harrison P.J., and Stevens C.F. A Bayesian approach to short-term forecasting. Operations Research Quarterly 22 (1971) 341-362
32. Harvey A.C. Forecasting, Structural Time Series Models and the Kalman Filter (1989), Cambridge University Press, Cambridge, U.K.
33. Hastings W.K. Monte-Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
34. Ionides E.L., Breto C., and King A.A. Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences of the United States of America 103 (2006) 18438-18443
35. Ives A.R., Dennis B., Cottingham K.L., and Carpenter S.R. Estimating community stability and ecological interactions from time-series data. Ecological Monographs 73 (2003) 301-330
36. Jazwinski A.H. Stochastic Processes and Filtering Theory (1970), Academic Press, New York, New York, USA
37. Julier S.J., and Uhlmann J.K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92 (2004) 401-422
38. Kalman R.E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82 (1960) 34-45
39. Kitagawa G. Non-Gaussian state-space modeling of nonstationary time-series. Journal of the American Statistical Association 82 (1987) 1032-1041
40. Lele S.R., Dennis B., and Lutscher F. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecology Letters 10 (2007) 551-563
41. Lindley S.T. Estimation of population growth and extinction parameters from noisy data. Ecological Applications 13 (2003) 806-813
42. Little R.J., and Rubin D.B. Statistical Analysis with Missing Data (2002), John Wiley & Sons, New York, New York, USA
43. Liu J.S. Monte Carlo Strategies in Scientific Computing (2001), Springer, New York, USA
44. Liu J.S., Liang F.M., and Wong W.H. The multiple-try method and local optimization in metropolis sampling. Journal of the American Statistical Association 95 (2000) 121-134
45. Lubow B.C., Singer F.J., Johnson T.L., and Bowden D.C. Dynamics of interacting elk populations within and adjacent to Rocky Mountain National Park. Journal of Wildlife Management 66 (2002) 757-775
46. Mansson L., Ripa J., and Lundberg P. Time series modelling and trophic interactions: rainfall, vegetation and ungulate dynamics. Population Ecology 49 (2007) 287-296
47. Meinhold R.J., and Singpurwalla N.D. Understanding the Kalman filter. American Statistician 37 (1983) 123-127
48. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21 (1953) 1087-1092
49. Meyer R., and Millar R.B. Bayesian stock assessment using a state-space implementation of the delay difference model. Canadian Journal of Fisheries and Aquatic Sciences 56 (1999) 37-52
50. Millar R.B., and Meyer R. Bayesian state-space modeling of age-structured data: fitting a model is just the beginning. Canadian Journal of Fisheries and Aquatic Sciences 57 (2000) 43-50
51. Mo X.G., Chen J.M., Ju W.M., and Black T.A. Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter. Ecological Modelling 217 (2008) 157-173
52. Newman K.B. State-space modeling of animal movement and mortality with application to salmon. Biometrics 54 (1998) 1290-1314
53. Newman K.B., Buckland S.T., Lindley S.T., Thomas L., and Fernandez C. Hidden process models for animal population dynamics. Ecological Applications 16 (2006) 74-86
54. 2 Enrichment experiment. New Phytologist 174 (2007) 823-834
55. Patterson T.A., Thomas L., Wilcox C., Ovaskainen O., and Matthiopoulos J. State-space models of individual animal movement. Trends in Ecology & Evolution 23 (2008) 87-94
56. Press W.H., Falnnery B.P., Teukolsky S.A., and Vettering W.T. Numerical Recipes in FORTRAN (1992), Cambridge University Press, Cambridge, UK
57. Roweis S., and Ghahramani Z. Learning nonlinear dynamic systems using the expectation-maximization algorithm. In: Haykin S. (Ed). Kalman Filtering and Neural Networks (2001), John Wiley & Sons, New York, New York, United States 175-221
58. Royama T. Analytic Population Dynamics (1992), Chapman and Hall, London, UK
59. Royer F., Fromentin J.M., and Gaspar P. A state-space model to derive bluefin tuna movement and habitat from archival tags. Oikos 109 (2005) 473-484
60. Shumway R.H., and Stoffer D.S. Time Series Analysis and Its Applications (2000), Springer, New York, New York, USA
61. Spiegelhalter D.A., Thomas A., and Best N. WinBUGS User Manual (2000), MRC Biostatistics Unit, Cambridge, UK
62. Stenseth N.C., Viljugrein H., Jedrzejewski W., Mysterud A., and Pucek Z. Population dynamics of Clethrionomys glareolus and Apodemus flavicollis: seasonal components of density dependence and density independence. Acta Theriologica 47 (2002) 39-67
63. Stoffer D.S., and Wall K.D. Bootstrapping state-space models - Gaussian maximum-likelihood-estimation and the Kalman filter. Journal of the American Statistical Association 86 (1991) 1024-1033
64. Thomas L., Buckland S.T., Newman K.B., and Hardwood J. A unified framework for modelling wildlife population dynamics. Australian & New Zealand Journal of Statistics 47 (2005) 19-34
65. Viljugrein H., Stenseth N.C., Smith G.W., and Steinbakk G.H. Density dependence in North American ducks. Ecology 86 (2005) 245-254
66. Wan E.A., and van de Merwe R. The unscented Kalman filter. In: Haykin S. (Ed). Kalman Filtering and Neural Networks (2001), John Wiley & Sons, New York, New York, United States 221-281
67. Wang G.M. On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models. Ecological Modelling 200 (2007) 521-528
68. Wang G., and Getz L.L. State-space models for stochastic and seasonal fluctuations of vole and shrew populations in east-central Illinois. Ecological Modelling 207 (2007) 189-196
69. Wang G.M., Hobbs N.T., Boone R.B., Illius A.W., Gordon I.J., Gross J.E., and Hamlin K.L. Spatial and temporal variability modify density dependence in populations of large herbivores. Ecology 87 (2006) 95-102
70. Wang, G.M., Hobbs, N.T., Boone, R.B., Illius, A.W., Gordon, I.J., Gross, J.E. and Hamlin, K.L., in press. Density dependence in northern ungulates: interactions with predation and resources. Population Ecology 51, 123-132.
71. West M., and Harrison J. Bayesian Forecasting and Dynamic Models (1997), Springer-Verlag, New York, New York, USA
72. Wikle C.K., and Berliner L.M. A Bayesian tutorial for data assimilation. Physica D-Nonlinear Phenomena 230 (2007) 1-16
73. Williams C.K., Ives A.R., and Applegate R.D. Population dynamics across geographical ranges: time-series analyses of three small game species. Ecology 84 (2003) 2654-2667
74. Zeng Z., Nowierski R.M., Taper M.L., Dennis B., and Kemp W.P. Complex population dynamics in the real world: modeling the influence of time-varying parameters and time lags. Ecology 79 (1998) 2193-2209