A sequential Monte Carlo approach for marine ecological prediction

Journal Article


This study considers the problem of marine ecological prediction in the context of online estimation and forecasting. Process oriented dynamic ecosystem models are combined with marine observations. The nonlinear, nonGaussian state space model provides the statistical framework. The associated filtering (nowcasting) and prediction (forecasting) problems are addressed via sequential Monte Carlo methods, in this instance a sequential importance resampler combined with Metropolis‐Hastings MCMC. The specific focus is on a prototypical marine ecosystem model comprised of four interacting populations (phytoplankton, zooplankton, nutrients and detritus; PZND) whose co‐evolution is described by system of coupled nonlinear differential equations. Stochastic environmental variation is introduced through a stochastic growth parameter, as well as through dynamical noise in the state evolution equations. The dynamic behaviour of this stochastic ecosystem model is complex: it regularly transitions through a Hopf bifurcation and exhibits episodic blooms of variable magnitude and duration. The model is applied to a case with weak seasonality, that is the oceanic mixed layer in the eastern equatorial Pacific. A partially observed state is considered comprised of a five year satellite (SeaWiFS) derived time series of ocean phytoplankton concentration at 12°N 95°W. Filtering estimates for the ecosystem state and a dynamic parameter were obtained using the sequential Monte Carlo approach. These showed predictor‐corrector behaviour at observation times, including abrupt shifts in the median level after forecasts over measurement void. A corresponding variance (also skewness and kurtosis) growth and subsequent collapse was also seen. Forecasting experiments indicate some negative bias, and suggest there is predictive skill for forecasts out to 10–15 days. Copyright © 2005 John Wiley & Sons, Ltd.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.