FN Archimer Export Format
PT J
TI Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression
BT 
AF SOUDANT, Dominique
   BELIAEFF, Benoit
   THOMAS, G
AS 1:1;2:1;3:2;
FF 1:;2:;3:;
C1 IFREMER, BP 21105, F-44311 NANTES 03, FRANCE.
   INSERM, U444, F-75571 PARIS 12, FRANCE.
C2 IFREMER, FRANCE
   INSERM, FRANCE
IN WOS Ifremer jusqu'en 2018
IF 1.928
TC 9
UR https://archimer.ifremer.fr/doc/00337/44784/44490.pdf
LA English
DT Article
DE ;phytoplankton;Dinophysis;time series;regression;dynamic;Bayesian
AB 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.
PY 1997
SO Marine Ecology Progress Series
SN 0171-8630
PU Inter-research
VL 156
UT A1997XZ98100007
BP 67
EP 74
DI 10.3354/meps156067
ID 44784
ER

EF