TY - JOUR
T1 - Identifying the factors determining blooms of cyanobacteria in a set of shallow lakes
AU - Descy, J.-P.
AU - Leprieur, F
AU - Pirlot, S
AU - Leporcq, B
AU - Van Wichelen, J
AU - Peretyatko, A
AU - Teissier, S
AU - Codd, Geoff
AU - Triest, L
AU - Vijverman, Wim
AU - Wilmotte, Annick
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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.
KW - Boosted regression trees
KW - Eutrophication
KW - Lake management
KW - Modelling
UR - http://www.scopus.com/inward/record.url?scp=84971631251&partnerID=8YFLogxK
U2 - 10.1016/j.ecoinf.2016.05.003
DO - 10.1016/j.ecoinf.2016.05.003
M3 - Article
SN - 1574-9541
VL - 34
SP - 129
EP - 138
JO - Ecological Informatics
JF - Ecological Informatics
ER -