Environmental factors affecting maerl bed structure in Brittany (France)

This study used a large spatial scale approach in order to better quantify the relationships between maerl bed structure and a selection of potentially forcing physical factors. Data on maerl bed structure and morpho-sedimentary characteristics were obtained from recent oceanographic surveys using underwater video recording and grab sampling. Considering the difficulties in carrying out real-time monitoring of highly variable hydrodynamic and physicochemical factors, these were generated by three-dimensional numerical models with high spatial and temporal resolution. The BIOENV procedure indicated that variation in the percentage cover of thalli can best be explained (correlation = 0.76) by a combination of annual mean salinity, annual mean nitrate concentration and annual mean current velocity, while the variation in the proportion of living thalli can best be explained (correlation = 0.47) by a combination of depth and mud content. Linear relationships showed that the percentage cover of maerl thalli was positively correlated with nitrate concentration (R2 = 0.78, P < 0.01) and negatively correlated with salinity (R2 = 0.81, P < 0.01), suggesting a strong effect of estuarine discharge on maerl bed structure, and also negatively correlated with current velocity (R2 = 0.81, P < 0.01). When maerl beds were deeper than 10 m, the proportion of living thalli was always below 30% but when they were shallower than 10 m, it varied between 4 and 100%, and was negatively correlated with mud content (R2 = 0.53, P < 0.01). On the other hand, when mud content was below 10%, the proportion of living thalli showed a negative correlation with depth (R2 = 0.84, P < 0.01). This large spatial scale explanation of maerl bed heterogeneity provides a realistic physical characterization of these ecologically interesting benthic habitats and usable findings for their conservation and management.

Determination of thresholds in marine molluscs as an alternative to the Environmental Quality Standards in marine water defined in the Water Framework Directive

The Water Framework Directive (WFD) establishes Environmental Quality Standards (EQS) in marine water for 34 priority substances. Among these substances, 25 are hydrophobic and bioaccumulable (2 metals and 23 organic compounds). For these 25 substances, monitoring in water matrix is not appropriate and an alternative matrix should be developed. Bivalve mollusks, particularly mussels (Mytilus edulis, Mytilus galloprovincialis), are used by Ifremer as a quantitative biological indicator since 1979 in France, to assess the marine water quality. This study has been carried out in order to determine thresholds in mussels at least as protective as EQS in marine water laid down by the WFD. Three steps are defined: - Provide an overview of knowledges about the relations between the concentrations of contaminants in the marine water and mussels through bioaccumulation factor (BAF) and bioconcentration factor (BCF). This allows to examine how a BCF or a BAF can be determined: BCF can be determined experimentally (according to US EPA or ASTM standards), or by Quantitative Activity-Structure Relationship models (QSAR): four equations can be used for mussels. BAF can be determined by field experiment; but none standards exists. It could be determined by using QSAR but this method is considered as invalid for mussels, or by using existing model: Dynamic Budget Model, but this is complex to use. - Collect concentrations data in marine water (Cwater) in bibliography for those 25 substances; and compare them with concentration in mussels (Cmussels) obtained through French monitoring network of chemicals contaminants (ROCCH) and biological integrator network RINBIO. According to available data, this leads to determine the BAF or the BCF (Cmussels /Cwater) with field data. - Compare BAF and BCF values (when available) obtained with various methods for these substances: BCF (stemming from the bibliography, using experimental process), BCF calculated by QSAR and BAF determined using field data. This study points out that experimental BCF data are available for 3 substances (Chlorpyrifos, HCH, Pentachlorobenzene). BCF by QSAR can be calculated for 20 substances. The use of field data allows to evaluate 4 BAF for organic compounds and 2 BAF for metals. Using these BAF or BCF value, thresholds in shellfish can be determined as an alternative to EQS in marine water.

Chronic Polyaromatic Hydrocarbon (PAH) Contamination Is a Marginal Driver for Community Diversity and Prokaryotic Predicted Functioning in Coastal Sediments

Benthic microorganisms are key players in the recycling of organic matter and recalcitrant compounds such as polyaromatic hydrocarbons (PAHs) in coastal sediments. Despite their ecological importance, the response of microbial communities to chronic PAH pollution, one of the major threats to coastal ecosystems, has received very little attention. In one of the largest surveys performed so far on coastal sediments, the diversity and composition of microbial communities inhabiting both chronically contaminated and non-contaminated coastal sediments were investigated using high-throughput sequencing on the 18S and 16S rRNA genes. Prokaryotic alpha-diversity showed significant association with salinity, temperature, and organic carbon content. The effect of particle size distribution was strong on eukaryotic diversity. Similarly to alpha-diversity, beta-diversity patterns were strongly influenced by the environmental filter, while PAHs had no influence on the prokaryotic community structure and a weak impact on the eukaryotic community structure at the continental scale. However, at the regional scale, PAHs became the main driver shaping the structure of bacterial and eukaryotic communities. These patterns were not found for PICRUSt predicted prokaryotic functions, thus indicating some degree of functional redundancy. Eukaryotes presented a greater potential for their use as PAH contamination biomarkers, owing to their stronger response at both regional and continental scales.