Long-term trends in the phytoplankton community along the French coast of the English Channel and southern Bight of the North Sea were studied and related to physico-chemical factors and large-climatic indices. Phytoplankton and hydrological data were acquired through three transects. Sampling took place between 1992 and 2011 as part of the French Phytoplankton Monitoring Network (REPHY) and the Regional Nutrients Monitoring Network (SRN). Trends in time-series were identified with dynamic linear models tailored to environmental monitoring data characteristics (e.g. irregular sampling frequency, missing data). Temporal and spatial patterns in the phytoplankton community were explained with a partial triadic analysis. Relationships between the phytoplankton community composition, environmental factors, and climatic indices were assessed using a redundancy analysis (RDA). The analyses revealed long-term changes in the community composition, characterized by a temporal structure that remained common to all transects. The abundance of some groups of taxa such as the one composed by Gymnodinium and Gyrodinium, as well as the group of Pseudo-nitzschia increased during the study period, whereas the abundance of other taxa as for example Guinardia and the group of Coscinodiscus and Stellarima globally decreased. More generally, the proportion of dinoflagellates relative to diatoms increased. Trends in environmental variables were also observed in most sites and related to decreases in nutrient concentrations and an increasing trend in salinity. The RDA indicated that the Atlantic Multidecadal Oscillation index and salinity were the main factors defining the temporal structure of the phytoplankton community. This suggests that variations observed in the phytoplankton community are linked to hydro-climatic changes in the coastal environment.

King scallop contamination (Pecten maximus) by domoic acid, a neurotoxin produced by some species of the diatom Pseudo-nitzschia, is highly problematic because of its lengthy retention in the bivalve tissue, leading to prolonged fishery closures. Data collected within the French Phytoplankton and Phycotoxin monitoring network (REPHY) over the 1995-2012 period were used to characterize the seasonal dynamics and the interannual variability of P.-nitzschia spp. blooms as well as the contamination of king scallop fishing grounds, in six contrasted bays distributed along the French Atlantic coast and English Channel. Monitoring revealed that these toxic events have become more frequent since the year 2000, but with varying magnitudes, frequencies and timing depending on the bay. Two bays, located in southern Brittany, exhibited both recurrent contaminations and high P.-nitzschia abundances. The Brest bay and the Seine bay were intermittently affected. The Pertuis Breton exhibited only one major toxic event related to an exceptionally intense bloom of P.-nitzschia in 2010, and the Saint Brieuc bay neither showed significant contamination nor high P.-nitzschia abundance. While high P.-nitzschia abundance appeared to be correlated to scallop toxicity, this study highlights the difficulty in linking P.-nitzschia spp. blooms to king scallop contamination through monitoring. Indeed, P.-nitzschia was determined at the genus level and data regarding species abundances and their toxicity levels are an absolute prerequisite to further assess the environmental control of ASP events. As results describe distinct P.-nitzschia bloom dynamics along the French coast, this may suggest distinct controlling factors. They also revealed that major climatic events, such as the winter storm Xynthia in 2010, can trigger toxicity in P.-nitzschia over a large spatial scale and impact king scallop fisheries all along the coast.

Within the framework of research aimed at using genetic methods to evaluate harmful species distribution and their impact on coastal ecosystems, a portion of the ITS1rDNA of Alexandrium minutum was amplified by real-time PCR from DNA extracts of superficial (1-3cm) sediments of 30 subtidal and intertidal stations of the Bay of Brest (Brittany, France), during the winters of 2013 and 2015. Cell germinations and rDNA amplifications of A. minutum were obtained for sediments of all sampled stations, demonstrating that the whole bay is currently contaminated by this toxic species. Coherent estimations of ITS1rDNA copy numbers were obtained for the two sampling cruises, supporting the hypothesis of regular accumulation of A. minutum resting stages in the south-eastern, more confined embayments of the study area, where fine-muddy sediments are also more abundant. Higher ITS1rDNA copy numbers were detected in sediments of areas where blooms have been seasonally detected since 2012. This result suggests that specific genetic material estimations in superficial sediments of the bay may be a proxy of the cyst banks of A. minutum. The simulation of particle trajectory analyses by a Lagrangian physical model showed that blooms occurring in the south-eastern part of the bay are disconnected from those of the north-eastern zone. The heterogeneous distribution of A. minutum inferred from both water and sediment suggests the existence of potential barriers for the dispersal of this species in the Bay of Brest and encourages finer analyses at the population level for this species within semi-enclosed coastal ecosystems.

Alternatives to Species-Level Diatom Analysis qualitative analysis (i.e. species presence/absence) or, in alternatively, a genus-level analysis retaining abundance data may be sufficient to describe basic spatial differences in estuarine intertidal flats. However, if seasonal variations in mudflat diatom assemblage structure are to be detected, species-level abundance data are still necessary.

No abstract

Global changes have led to a renewed interest in time series of environmental monitoring. In France, for example, the French Research Institute for the Exploitation of the Sea (Ifremer) has been managing for 40 years several networks with hundreds of active sites, with annual to fortnightly sampling frequencies, measuring dozens of variables. These long-term datasets are difficult to analyse due to their characteristics (e.g. missing data, outliers, changes in sampling frequency, shifts).For this large number of time series, this paper proposes a semi-automatic procedure based on Dynamic Linear Models, detailed from data pre-processing (e.g. time unit definition, aggregations, transformations), through model specification, automatic and manual intervention, outlier and shift handling, to model hypothesis testing.When applied to three time series combining the above features, the results showed that missing data and changes in sampling frequency were adequately handled. Outliers and structural breaks were identified automatically, but also added manually. Highlighted shifts were identified as artefactual (e.g. probe drift), anthropogenic (e.g. ministerial decree) and ecological changes (e.g. storm impact).Finally, the presented treatment has been successfully applied routinely to more than 19,000 time series with a common and simple model structure. The broad theoretical framework offered by dynamic linear models opens up fruitful perspectives for improving and extending the results presented here, in particular for dealing with measurement quantification limits and time-varying observation variances.