FN Archimer Export Format
PT J
TI High resolution overview of phytoplankton spectral groups and hydrological conditions in the eastern English Channel using unsupervised clustering
BT 
AF LEFEBVRE, Alain
   POISSON-CAILLAULT, Emilie
AS 1:1;2:1,2;
FF 1:PDG-ODE-LITTORAL-LERBL;2:;
C1 IFREMER, Lab Environm & Ressources, F-62200 Boulogne Sur Mer, France.
   Univ Littoral Cote dOpale, LISIC, EA 4491, F-62228 Calais, France.
C2 IFREMER, FRANCE
   UNIV LITTORAL COTE D'OPALE, FRANCE
SI BOULOGNE
SE PDG-ODE-LITTORAL-LERBL
IN WOS Ifremer UPR
   copubli-france
   copubli-univ-france
IF 2.326
TC 7
UR https://archimer.ifremer.fr/doc/00479/59043/61793.pdf
LA English
DT Article
CR DYPHYMA I ET II
   PHYCO
BO Côtes De La Manche
DE ;Clustering;Spectral fluorescence;Phaeocystis globosa;English Channel Ferry Box;MSFD;Marine Strategy Framework Directive
AB As we move towards shipboard-underway and automated systems for monitoring water quality and assessing ecological status, there is a need to evaluate how effective the existing monitoring systems are, and how we could improve them. Considering the existing limitations for processing numerous and complex data series generated from automated systems, and because of processes involved in phytoplankton blooms, this paper proposes a data-driven evaluation of an unsupervised classifier to optimize the way we track phytoplankton, including harmful algal blooms (HABs), and to identify the main associated hydrological conditions. We used in situ data from a portable flow-through automatic measuring system coupled with a multi-fixed-wavelength fluorometer implemented in the eastern English Channel during a bloom of Phaeocystis globosa (high biomass, non-toxic HAB species). This combination of technologies allowed high resolution online hydrographical and biological measurements, including spectral fluorescence as a means of quantifying phytoplankton biomass and simplifying the phytoplankton community structure inference. An unsupervised spectral clustering method was applied to this multi-parameter high-resolution time series, which allowed discrimination under near real-time of 6 to 33 contrasting water masses based on their abiotic and biotic characteristics. In addition, areas subject to extreme events such as HABs could be precisely identified, so controlling factors or their direct and indirect effects could be hierarchized. Considering the benefits and limitations of such a strategy, future applications of such methods will be important in the context of implementing the Marine Strategy Framework Directive. 
PY 2019
PD JAN
SO Marine Ecology Progress Series
SN 0171-8630
PU Inter-research
VL 608
UT 000456206700006
BP 73
EP 92
DI 10.3354/meps12781
ID 59043
ER

EF