Identifying the factors determining blooms of cyanobacteria in a set of shallow lakes

J.-P. Descy, F Leprieur, S Pirlot, B Leporcq, J Van Wichelen, A Peretyatko, S Teissier, Geoff Codd, L Triest, Wim Vijverman, Annick Wilmotte

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    33 Citations (Scopus)


    There is a strong interest in developing a capacity to predict the occurrence of cyanobacteria blooms in lakes and to identify the measures to be taken to reduce water quality problems associated with the occurrence of potentially harmful taxa. Here we conducted a weekly to bi-weekly monitoring program on five shallow eutrophic lakes during two years, with the aim of gathering data on total cyanobacterial abundance, as estimated from marker pigments determined by HPLC analysis of phytoplankton extracts. We also determined bloom composition and measured weather and limnological variables. The most frequently identified taxa were Aphanizomenon flos-aquae, Microcystis aeruginosa, Planktothrix agardhii and Anabaena spp. We used the data base composed of a total of 306 observations and an adaptive regression trees method, the boosted regression tree (BRT), to develop predictive models of bloom occurrence and composition, based on environmental conditions. Data processing with BRT enabled the design of satisfactory prediction models of cyanobacterial abundance and of the occurrence of the main taxa. Phosphorus (total and soluble reactive phosphate), dissolved inorganic nitrogen, epilimnion temperature, photoperiod and euphotic depth were among the best predictive variables, contributing for at least 10% in the models, and their relative contribution varied in accordance with the ecological traits of the taxa considered. Meteorological factors (wind, rainfall, surface irradiance) had a significant role in species selection. Such results may contribute to designing measures for bloom management in shallow lakes.

    Original languageEnglish
    Pages (from-to)129-138
    Number of pages10
    JournalEcological Informatics
    Publication statusPublished - 1 Jul 2016


    • Boosted regression trees
    • Eutrophication
    • Lake management
    • Modelling


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