A sequential Monte Carlo approach for marine ecological prediction

Environmetrics

Volumn 17, Issue 5, 2006, Pages 435-455

  Dowd, Michael ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Complex dynamics; Data assimilation; Marine ecosystem model; Nonlinear time series; Particle filters; State space models

Indexed keywords

DETRITUS; ECOSYSTEM MODELING; MARINE ECOSYSTEM; MONTE CARLO ANALYSIS; PHYTOPLANKTON; PREDICTION; ZOOPLANKTON;

EID: 33745987359     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.780     Document Type: Article

References (54)

1. Allen JI, Eknes M, Evensen G. 2003. An ensemble Kalman filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea. Annales Geophysicae 21: 399-411.
2. Alspach DL, Sorenson HW. 1972. Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control AC-17: 439-448.
3. Arulampalam MS, Maskell S, Gordon N, Clapp T. 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2): 174-188.
4. Bailey B, Doney SC, Lima ID. 2004. Quantifying the effects of dynamical noise on the predictability of a simple ecosystem model. Environmetrics 15: 337-355.
5. Bertino L, Evensen G, Wackernagel H. 2003. Sequential data assimilation techniques in oceanography. International Statistical Review 71(2): 223-241.
6. Bjørnstad ON, Grenfell BT. 2001. Noisy clockwork: time series analysis of population fluctuations in animals. Science 293: 638-643.
7. Campbell JW. 1995. The lognormal distribution as a model for bio-optical variability in the sea. Journal of Geophysical Research 100: 13237-13254.
8. Carlin BP, Chib S. 1995. Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B 57(3): 473-484.
9. Carlin BP, Polson NG, Stoffer DS. 1992. A Monte Carlo approach to nonnormal and nonlinear state space modelling. Journal of the American Statistical Association 87: 493-500.
10. Chib S, Greenberg E. 1995. Understanding the Metropolis-Hastings Algorithm. The American Statistician 49(4): 327-335.
11. Cloern JE, Grenz C, Vidergar-Lucas L. 1995. An empirical model of the plankton chlorophyll: carbon ratio - the conversion factor between productivity and growth rate. Limnology and Oceanography 40(7): 1313-1321.
12. Cullen JJ, Lewis MR. 1988. The kinetics of algal photoadaptation in the context of vertical mixing. Journal of Plankton Research 10: 1039-1063.
13. Cullen JJ, Ciotti AM, Davis RF, Lewis MR. 1997. Optical detection and assessment of algal blooms. Limnololgy and Oceanography 42: 1223-1239.
14. Denman KL, Gargett AE. 1983. Time and space scales of vertical mixing and advection of phytoplankton in the upper ocean. Limnology and Oceanography 28(5): 801-815.
15. Doucet A, de Freitas N, Gordon N. 2001. Sequential Monte Carlo Methods in Practice. Springer: New York; 581.
16. Dowd M. 2005. A biophysical coastal ecosystem model for assessing environmental effects of marine bivalve aquaculture. Ecological Modelling 183(2-3): 323-346.
17. Dowd M, Meyer R. 2003. A Bayesian approach to the ecosystem inverse problem. Ecological Modelling 169: 39-55.
18. Dowd M, Martin JL, LeGresley MM, Hanke A, Page FH. 2003. Interannual variability in a plankton time series. Environmetrics 14(1): 73-86.
19. Edwards AM. 2001. Adding detritus to a nutrient-phytoplankton-zooplankton model: a dynamical systems approach. Journal of Plankton Research 23: 389-413.
20. Edwards AM, Platt T, Sathyendranath S. 2004. The high nutrient, low chlorophyll regime of the ocean: limits on biomass and nitrate before and after iron enrichment. Ecological Modelling 171: 103-125.
21. Evans SN, Stark PB. 2002. Inverse problems as statistics. Inverse Problems 18: R55-R97.
22. Evensen G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynamics 53: 343-367.
23. Fasham MJR. 1993. Modelling the marine biota. In The Global Carbon Cycle, Heimann M (ed.). Spinger-Verlag: Berlin; 457-504.
24. Fennell W, Neumann T. 2004. Introduction to the Modelling of Marine Ecosystems. Elsevier: Amsterdam; 330.
25. Gilks WR, Berzuini C. 2001. Following a moving target - Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society B 63(1): 127-146.
26. Gordon NJ, Salmond DJ, Smith AFM. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F 140(2): 107-113.
27. Gould WJ. 2003. WOCE and TOGA the foundations of the Global Ocean Observing System. Oceanography 16(4): 24-30.
28. Harmon R, Challenor P. 1997. A Markov chain Monte Carlo method for estimation and assimilation into models. Ecological Modelling 101: 41-59.
29. Harvey AE. 1989. Forecasting, structural time series models and the Kalman filter. Cambridge University Press: Cambridge; 554.
30. Hofmann EE, Lascara CM. 1998. Overview of interdisciplinary modeling for marine ecosystems. In The Sea: Ideas and Observations on Progress in the Study of the Seas. The Global Coastal Ocean: Processes and Methods, Vol. 10, Brink KH, Robinson AR (eds). Wiley: New York.
31. Hooker SB, McClain CR. 2000. The calibration and validation of SeaWiFS data. Progress in Oceanography 3-4: 427-465.
32. Jazwinski AH. 1970. Stochastic Processes and Filtering Theory. Academic Press: New York; 376.
33. Kalman RE. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, Transactions AMSE, Series D 82: 35-45.
34. Kitagawa G. 1998. A self-organizing state space model. Journal of the American Statistical Association 93(443): 1203-1215.
35. Kitagawa G. 1996. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5: 1-25.
36. Kitagawa G. 1987. Non-Gaussian state-space modeling of nonstationary time series. Journal of the American Statistical Association 82: 1032-1041.
37. Kong A, Liu JS, Wong WH. 1994. Sequential imputations and Bayesian missing data problems. Journal of the American Statistical Association 89(425): 278-288.
38. Lawson LM, Sptiz YH, Hofmann EE, Long RL. 1995. A data assimilation technique applied to a predator-prey model. Bulletin of Mathematical Biology 57: 593-617.
39. Lee DS, Chia NKK. 2002. A particle algorithm for sequential Bayesian parameter estimation and model selection. IEEE Transactions on Signal Processing 50(2): 326-336.
40. Losa SN, Kivman GA, Schroter J, Wenzel M. 2003. Sequential weak constraint parameter estimation in an ecosystem model. Journal of Marine Systems 43: 31-49.
41. Lui JS, Chen R. 1998. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93(443): 1032-1044.
42. Marion G, Renshaw E, Gibson G. 2000. Stochastic modelling of environmental variation for biological populations. Theoretical Population Biology 57: 197-217.
43. McClain CR, Barnes RA, Eplee RE, et al. 2000. SeaWiFS Postlaunch Calibration and Validation Analyses, Part 2. NASA Technical Memorandum 2000-206892, Vol. 10. Hooker SB, Firestone ER (eds). NASA Goddard Space Flight Center, 57.
44. Meyer R, Christensen NL. 2000. Bayesian reconstruction of chaotic dynamical systems. Physical Review E 62: 3535-3542.
45. Monahan AH, Denman KL. 2004. Impacts of atmospheric variability on a coupled upper-ocean/ecosystem model of the subarctic Northeast Pacific. Global Biogeochemical Cycles 18: GB2010.
46. Narvik LJ, Evensen G. 2003. Assimilation of ocean color data into a biochemical model of the North Atlantic. Part 2. Statistical Analysis. Journal of Marine Systems 40-41: 155-169.
47. O'Reilly JE, Maritorena S, Mitchell BG, et al. 1998. Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research 103: 24937-24953.
48. Seigel DA, Karl DM, Michaels AF. 2001. Interpretations of biogeochemical processes for the US JGOFS Bermuda and Hawaii time series sites. Deep Sea Research II 8: 1403-1404.
49. Steele JH, Henderson EW. 1981. A simple plankton model. American Naturalist 117: 676-691.
50. Talley LD, Fryer G, Lumpkin R. 2005. Physical Oceanography of the Topical Pacific. In Geography of the Pacific Islands, pp. 19-32, Rapaport M (ed.).
51. Thompson KR, Dowd M, Lu Y, Smith B. 2000. Oceanographic data assimilation and regression analysis. Environmetrics 11: 183-196.
52. Tunnicliffe V, Dewey R, Smith D. 2003. Research plans for a cabled seafloor observatory in western Canada. Oceanography 16(4): 53-59.
53. West M, Harrison PJ, Migon HS. 1985. Dynamic generalized linear models and Bayesian forecasting (with discussion). Journal of the American Statistical Association 80: 73-97.
54. Vallino JJ. 2000. Improving marine ecosystem models: use of data assimilation and mesocosm experiments. Journal of Marine Research 58: 117-164.