FN Archimer Export Format
PT J
TI How to learn more about hydrological conditions and phytoplankton dynamics and diversity in the eastern English Channel and the Southern Bight of the North Sea: the Suivi Régional des Nutriments data set (1992–2021)
BT 
AF Lefebvre, Alain
   Devreker, David
AS 1:1;2:1;
FF 1:PDG-ODE-LITTORAL-LERBL;2:PDG-ODE-LITTORAL-LERBL;
C1 Ifremer, Unité Littoral, Laboratoire Environnement et Ressources, 150 quai Gambetta, BP 699, 62321 Boulogne-sur-mer, France
C2 IFREMER, FRANCE
SI BOULOGNE
SE PDG-ODE-LITTORAL-LERBL
IN WOS Ifremer UPR
   DOAJ
IF 11.4
TC 2
UR https://archimer.ifremer.fr/doc/00787/89868/95342.pdf
   https://archimer.ifremer.fr/doc/00787/89868/100497.pdf
   https://archimer.ifremer.fr/doc/00787/89868/100498.zip
LA English
DT Article
AB This article describes the historical data series produced by the SRN network managed by Ifremer. Since 1992, the SRN (‘Suivi Régional des Nutriments’ in French; Regional Nutrients Monitoring Programme) network has been analysing phytoplankton species and measuring twelve physicochemical parameters at ten different stations distributed along three different transects located in the Eastern English Channel, and the Southern Bight of the North Sea. The SRN collects a maximum of 184 samples per year and detects up to 281 taxa, including harmful algal bloom species (HABs), with a bi-weekly to monthly sampling frequency (depending on the location and the season). The objectives of this monitoring program are to assess the influence of continental inputs on the marine environment, and their implications on possible eutrophication processes. It also aims to estimate the effectiveness of development and management policies in the marine coastal zone. The regular acquisition of data allows the establishment of a long-term monitoring of the evolution of coastal water quality, as well as the observation of the consequences of large-scale alterations and modifications that are more related to regional anthropogenic activities. This paper provides an overview of the main characteristics of SRN data (descriptive statistics and data series main patterns) as well as an analysis of temporal trends using specific numerical tools available as an R package to help make use of such data. Main results of several research projects are also highlighted providing the readers with examples of what is doable with such a data set. 
PY 2023
PD MAR
SO Earth System Science Data
SN 1866-3508
PU Copernicus GmbH
VL 15
IS 3
UT 000946512900001
BP 1077
EP 1092
DI 10.5194/essd-2022-146
ID 89868
ER

EF