Better inferences from population-dynamics experiments using Monte Carlo state-space likelihood methods

Ecology

Volumn 84, Issue 11, 2003, Pages 3064-3077

  De Valpine, Perry ^a,b  

^a NATIONAL CENTER FOR ECOLOGICAL ANALYSIS AND SYNTHESIS   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Kernel density estimation; Measurement error; Monte Carlo maximum likelihood; Population dynamics; Population model fitting; Process error; Stage structured demography; State space model, nonlinear; Statistical power

Indexed keywords

MAXIMUM LIKELIHOOD ANALYSIS; MONTE CARLO ANALYSIS; POPULATION DYNAMICS; POPULATION MODELING;

EID: 1342287624     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/02-0039     Document Type: Article

References (64)

1. Bjornstad, O. N., M. Begon, N. C. Stenseth, W. Falck, S. M. Sait, and D. J. Thompson. 1998. Population dynamics of the indian meal moth: demographic stochasticity and delayed regulatory mechanisms. Journal of Animal Ecology 67:110-126.
2. Bjornstad, O. N., J.-M. Fromentin, N. C. Stenseth, and J. Gjosaeter. 1999. Cycles and trends in cod populations. Proceedings of the National Academy of Sciences (USA) 96: 5066-5071.
3. Bjornstad, O. N., and B. T. Grenfell. 2001. Noisy clockwork: time series analysis of population fluctuations in animals. Science 193:638-643.
4. Bjornstad, O. N., S. M. Sait, N. C. Stenseth, D. J. Thompson, and M. Begon. 2001. The impact of specialized enemies on the dimensionality of host dynamics. Nature 409:1001-1006.
5. Carlin, B. P., N. G. Polson, and D. S. Stoffer. 1992. A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association 87:493-500.
6. de Valpine, P., and A. Hastings. 2002. Fitting population models with process noise and observation error. Ecological Monographs 72:57-76.
7. Dennis, B., R. A. Desharnais, J. M. Cushing, and R. Costantino. 1995. Nonlinear demographic dynamics: mathematical models, statistical methods, and biological experiments. Ecological Monographs 65:261-281.
8. Dennis, B., R. A. Desharnais, J. M. Cushing, S. M. Henson, and R. F. Costantino. 2001. Estimating chaos and complex dynamics in an insect population. Ecological Monographs 71:277-303.
9. Doucet, A., N. de Freitas, and N. Gordon, editors. 2001. Sequential Monte Carlo methods in practice. Springer-Verlag, New York, New York, USA.
10. Durbin, J., and S. J. Koopman. 1997. Monte Carlo maximum likelihood estimation for non-Gaussian state space models. Biometrika 84:669-684.
11. Durbin, J., and S. J. Koopman. 2000. Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. Journal of the Royal Statistical Society, B 62:3-56.
12. Ellner, S. P., B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. T. Grenfell, and D. W. Nychka. 1998. Noise and nonlinearity in measles epidemics: combining mechanistic and statistical approaches to population modeling. American Naturalist 15:425-440.
13. Ellner, S. P., et al. 2001. Habitat structure and population persistence in an experimental community. Nature 412: 538-543.
14. Freeman, S. N., and G. P. Kirkwood. 1995. On a structural time series method for estimating stock biomass and recruitment from catch and effort data. Fisheries Research 22:77-98.
15. Geyer, C. J., and E. A. Thompson. 1992. Constrained Monte Carlo maximum likelihood for dependent data. Journal of the Royal Statistical Society, B 54:657-699.
16. Gibson, G., C. Gilligan, and A. Kleczkowski. 1999. Predicting variability in biological control of a plant-pathogen system using stochastic models. Proceedings of the Royal Society of London B 266:1743-1753.
17. Gibson, G. J., and E. Renshaw. 1998. Estimating parameters in stochastic compartmental models using Markov chain methods. IMA Journal of Mathematics Applied to Medicine and Biology 15:19-40.
18. Gilks, W. R., S. Richardson, and D. J. Spiegelhalter, editors. 1996. Markov Chain Monte Carlo in practice. Chapman and Hall, New York, New York, USA.
19. Gudmundsson, G. 1994. Time series analysis of catch-at-age observations. Applied Statistics 43:117-126.
20. Gurney, W., and R. Nisbet. 1998. Ecological dynamics. Oxford University Press, New York, New York, USA.
21. Gurney, W., R. Nisbet, and J. Lawton. 1983. The systematic formulation of tractable single-species population models incorporating age structure. Journal of Animal Ecology 52: 479-495.
22. Harvey, A. C. 1989. Forecasting, structural time series models, and the Kaiman filter. Cambridge University Press, Cambridge, UK.
23. Hilborn, R., and M. Mangel. 1997. The ecological detective: confronting models with data. Princeton University Press, Princeton, New Jersey, USA.
24. Ives, A. R., S. R. Carpenter, and B. Dennis. 1999. Community interaction webs and zooplankton responses to planktivory manipulations. Ecology 80:1405-1421.
25. Kareiva, P. 1987. Habitat fragmentation and the stability of predator-prey interactions. Nature 326:388-390.
26. Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, and S. N. Wood. 1999. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology 80:1789-1805.
27. Kimura, D. K., J. W. Balsiger, and D. H. Ito. 1996. Kalman filtering the delay-difference equation: practical approaches and simulations. Fishery Bulletin 94:678-691.
28. Kitagawa, G. 1987. Non-Gaussian state-space modeling of nonstationary time series (with discussion). Journal of the American Statistical Association 82:1032-1063.
29. Kitagawa, G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5:1-25.
30. Kitagawa, G. 1998. A self-organizing state-space model. Journal of the American Statistical Association 93:1203-1215.
31. Klug, J. L., J. Fischer, A. Ives, and B. Dennis. 2000. Compensatory dynamics in planktonic community responses to pH perturbations. Ecology 81:387-398.
32. Lundvall, D., R. Svanback, L. Persson, and P. Bystrom. 1999. Size-dependent predation in piscivores: interactions between predator foraging and prey avoidance abilities. Canadian Journal of Fisheries and Aquatic Sciences 56:1285-1292.
33. McAllister, M. K., E. K. Pikitch, A. E. Punt, and R. Hilborn. 1994. A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Canadian Journal of Fisheries and Aquatic Sciences 51:2673-2687.
34. Mendelssohn, R. 1988. Some problems in estimating population sizes from catch-at-age data. Fishery Bulletin 86: 617-630.
35. Metz, J., and O. Diekmann, editors. 1986. The dynamics of physiologically structured populations. Number 68 in Lecture Notes in Biomathematics. Springer-Verlag, Berlin, Germany.
36. Meyer, R., and R. B. Millar. 1999. Bayesian stock assessment using a state-space implementation of the delay difference model. Canadian Journal of Fisheries and Aquatic Sciences 56:37-52.
37. Millar, R. B., and R. Meyer. 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Applied Statistics 49: 327-342.
38. Peacor, S. D., and E. E. Werner. 2001. The contribution of trait-mediated indirect effects to the net effects of a predator. Proceedings of the National Academy of Sciences (USA) 98:3904-3908.
39. Pella, J. J. 1993. Utility of structural time series models and the Kalman Filter for predicting consequences of fishery actions. Pages 571-593 in G. Kruse, D. Eggers, R. Marasco, C. Pautzke, and T. J. Quinn II, editors. Proceedings of the International Symposium on Management Strategies for Exploited Fish Population. Alaska Sea Grant College Program Report Number 93-02. University of Alaska, Fairbanks, Alaska, USA.
40. Pitt, M. K., and N. Shephard. 1999. Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association 94:590-599.
41. Polis, G. 1991. Complex trophic interactions in deserts: an empirical critique of food web theory. American Naturalist 128:123-155.
42. Polis, G. A., and D. R. Strong. 1996. Food web complexity and community dynamics. American Naturalist 147:813-846.
43. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. 1992. Numerical recipes in C: the art of scientific computing. Second edition. Cambridge University Press, Cambridge, UK.
44. Quinn, T. J., II, and R. B. Deriso. 1999. Quantitative fish dynamics. Oxford University Press, New York, New York, USA.
45. Robert, C. P., and G. Casella. 1999. Monte Carlo statistical methods. Springer-Verlag, New York, New York, USA.
46. Rosenheim, J. A., H. K. Kaya, L. E. Ehler, J. J. Marois, and B. A. Jaffee. 1995. Intraguild predation among biological-control agents: theory and evidence. Biological Control 5: 303-335.
47. Schmitz, O. J., A. P. Beckerman, and K. M. O'Brien. 1997. Behaviorally mediated trophic cascades: effects of predation risk on food web interactions. Ecology 78:1388-1399.
48. Schnute, J. T. 1994. A general framework for developing sequential fisheries models. Canadian Journal of Fisheries and Aquatic Sciences 51:1676-1688.
49. Scott, D. 1992. Multivariate density estimation. John Wiley and Sons, New York, New York, USA.
50. Severini, T. A. 2000. Likelihood methods in statistics. Oxford University Press, New York, New York, USA.
51. Shephard, N., and M. K. Pitt. 1997. Likelihood analysis of non-Gaussian measurement time series. Biometrika 84: 653-667.
52. Sih, A., G. Englund, and D. Wooster. 1998. Emergent impacts of multiple predators on prey. Trends in Ecology and Evolution 13:350-355.
53. Silverman, B. 1986. Density estimation for statistics and data analysis. Chapman and Hall, New York, New York, USA.
54. Smith, A. 1992. Discussion of the paper by Geyer and Thompson. Journal of the Royal Statistical Society, B 54: 684-686.
55. Strauss, S. Y. 1991. Indirect effects in community ecology - their definition, study and importance. Trends in Ecology and Evolution 6:206-210.
56. Stuart, A., and J. K. Ord. 1991. Kendall's advanced theory of statistics. Volume 2. Classical inference and relationship. Fifth edition. Oxford University Press, New York, New York, USA.
57. Sullivan, P. J. 1992. A Kalman filter approach to catch-at-length analysis. Biometrics 48:237-257.
58. Tanizaki, H., and R. S. Mariano. 1998. Nonlinear and non-Gaussian state space modeling with Monte Carlo simulations. Journal of Econometrics 83:263-290.
59. Tong, H. 1990. Non-linear time series: a dynamical system approach. Oxford University Press, New York, New York, USA.
60. Tuljapurkar, S., and H. Caswell, editors. 1997. Structured-population models in marine, terrestrial, and freshwater systems. Chapman and Hall, New York, New York, USA.
61. Walde, S. J. 1991. Patch dynamics of a phytophagous mite population - effect of number of subpopulations. Ecology 72:1591-1598.
62. Wood, S. N. 1994. Obtaining birth and mortality patterns from structured population trajectories. Ecological Monographs 64:23-44.
63. Wood, S. N. 1997. Inverse problems and structured-population dynamics. Pages 555-586 in S. Tuljapurkar and H. Caswell, editors. Structured-population models in marine, terrestrial, and freshwater systems. Chapman and Hall, New York, New York, USA.
64. Wootton, J. T. 1994. The nature and consequences of indirect effects in ecological communities. Annual Review of Ecology and Systematics 25:443-466.