Explaining Dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Abstract

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 156:67-74 (1997) - doi:10.3354/meps156067 Explaining Dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression D. Soudant1,*, B. Beliaeff1, G. Thomas2 1IFREMER, B.P. 21105, F-44311 Nantes cedex 03, France 2INSERM U. 444, 27 rue de Chaligny, F-75571 Paris cedex 12, France *E-mail: dominique.soudant@ifremer.fr Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed. Phytoplankton · Dinophysis· Time series · Regression · Dynamic · Bayesian Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 156. Publication date: September 25, 1997 Print ISSN:0171-8630; Online ISSN:1616-1599 Copyright © 1997 Inter-Research.