IMIS | Lifewatch regional portal

You are here


[ report an error in this record ]basket (0): add | show Print this page

'Phytoplankton events' in French coastal waters during 1987-1997
Beliaeff, B.; Grosjean, P.; Belin, C.; Raffin, B.; Gailhard, I.; Durbec, J.-P. (2001). 'Phytoplankton events' in French coastal waters during 1987-1997. Oceanol. Acta 24(5): 425-433.
In: Oceanologica Acta. Elsevier/Gauthier-Villars: Montreuil. ISSN 0399-1784; e-ISSN 1878-4143, more
Peer reviewed article  

Available in  Authors | Dataset 

    Analysis > Mathematical analysis > Statistical analysis
    Aquatic communities > Plankton > Phytoplankton
    ANE, France, French Coast [Marine Regions]
Author keywords
    Spatio-temporal variability; Time-series segmentation

Authors  Top | Dataset 
  • Beliaeff, B.
  • Grosjean, P., more
  • Belin, C., more
  • Raffin, B.
  • Gailhard, I.
  • Durbec, J.-P., more

    This study aims to propose a tool to describe the long-term (10 years) variability of phytoplanktonic assemblages monitored by Rephy (French monitoring programme for phytoplankon and phycotoxins) in the English Channel, the Bay of Biscay and the Mediterranean French coastal waters. According to the sampling strategy (systematic survey, with a time-step of 1 or 2 weeks, and a between-sampling station distance ranging from less than one to several kilometres), the information content of the data is mainly relevant to the characterization of seasonal variability of populations at the mesoscale. For any given area, and for each of the 56 taxinomic units considered here, the data are thus processed in order to recognize the temporal succession of ‘phytoplankton events’: an event is defined by retaining qualitative information only. It encompasses the phases of sudden growth, high level of abundance and decline of a population. Times at which an event begins or ends are detected by using a time-series segmentation method, which allows to summarize the whole data set as a multivariate set of event occurrences. Categorizing observations in such a way also provides an efficient tool for the identification of different patterns of variability over long-term time scales, for instance: ‘recurrent events’ (e.g. populations generating events in a periodic-like manner), or ‘anomalies’ (e.g. of climatic origin). On an univariate basis, an ‘average’ event can be defined for each taxum, characterized by its within-year position, its duration, its magnitude and the associated deviations. On a multivariate basis, graphical representation of event successions could also allow between-year comparisons. A simple multivariate analysis was also used to describe the seasonal pattern of some taxa.

  • REPHY: Network Monitoring phytoplankton, more

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors | Dataset