Rapport final ONF

Larvae from Pacific oyster, Manila clam, European abalone and great scallop were subjected to two temperatures and two pH over the course of early development. RNAseq data was collected in order to evaluate which genes are modulated in response to stress.

Individuals from 5 populations were kept in common garden conditions in order to examine acclimation and adaptation to temperature in the honeycomb worm. Worms were exposed to 5 temperature treatments, and collected for RNAseq analysis. Gene expression patterns were then examined.

The BEAT+ tool builds on the EEA assessment tools developed and applied in the context of assessing the degree of contamination (CHASE+), eutrophication (HEAT+) and biodiversity (BEAT+) in Europe's seas. BEAT+ makes use of the same data sets and threshold values used in these assessments but recombines these in a new framework that addresses 'biodiversity condition'. BEAT+ has been designed to provide an assessment of the spatial variability of a range of biodiversity components by combining existing biodiversity indicators. The tool integrates data from normalised indicators to identify worst case status measures for different biodiversity components. The results are then linked to a standard gridE based Spatial Assessment Unit (SAU) which is used both for biodiversity and for pressures assessments (Andersen et al., 2014). These grid-based SAUs not only allow alignment of indicators for biodiversity and for pressures but provide a means for combining large assessment areas (e.g. for wide‐ranging species) with point data collected from biological surveys e.g. WFD monitoring. BEAT+ tool works by calculating a Biological Quality Ratio (BQR) which is an aggregated score of indicator outcomes within a grid square. To allow objective comparison, the indicator outcomes are normalised to a scale of 0 to 1, with five status classes at equal intervals on that scale (from Bad starting at 0, Poor at 0.2, Medium at 0.4, Good at 0.6 and High at 0.8). By this means, indicators based on different biological criteria can be aggregated in a consistent way. This metadata refers to dataset providing the results of classification of biodiversity status using the BEAT+ tool. The status is evaluated in five classes, where High and Good are recognised as ‘non-problem areas’ and Moderate, Poor and Bad are recognised as ‘problem areas’. The dataset covers: - BQR Assessment of all marine mammals combined (mainly focused on coastal and relatively stable inshore populations of seals, dolphins and porpoises) - BQR Assessment of seabirds and wading birds - BQR Assessment of commercial fish (as these have agreed targets defined on biomass and fishing mortality) - BQR Assessment of pelagic habitats - BQR Assessment of benthic habitats - BQR Assessment of worst-performing biodiversity groups - An overall synthesis of the Biological Quality Ratios (BQR) values (showing which are the worst -lowest- BQR values in each assessment grid cell. The ‘worst’ value is used here to identify the biological group most at risk, rather than averaging over all groups to avoid over-­emphasis on groups with more intensive monitoring). As reference, please consult the ETC/ICM Report 3/2019: Biodiversity in Europe's seas: https://www.eionet.europa.eu/etcs/etc-icm/products/biodiversity-in-europes-seas. The indicator BEAT+ Integrated Assessment Worst Case BQR has been used in the EEA report 17/2019 "Marine Messages II": https://www.eea.europa.eu/publications/marine-messages-2.

Ce projet s’attache à étudier les phénomènes Natech imputables à des inondations/tsunami en considérant deux échelles spatiales d’analyse : l’échelle du site industriel et l’échelle du territoire. Ces deux échelles permettent d’appréhender la problématique des Natechs d’une part d’un point de vue essentiellement « vulnérabilité » et d’autre part, grâce à une analyse plus globale et profonde qui fait résonner la notion de résilience territoriale. Le travail est basé sur une analyse a posteriori (au Japon) et a priori (en France) des pratiques de gestion des événements Natech auprès des parties prenantes (industriels, collectivités, services de l’état…). Pour cela, en France et au Japon, des questionnaires, des visites et des entretiens ont été réalisés sur des territoires touchés ou potentiellement concernés par le phénomène Natech inondation/tsunami. Ces données sont employées : -à l’échelle du site industriel, pour modéliser l’impact du phénomène naturel sur l’installation (par le biais notamment d’arbres de défaillances), puis produire deux outils d’aide à la décision (diagnostic de l’Etude de danger et diagnostic du Plan d’Opération Interne lors d’un événement Natech inondation) -à l’échelle du territoire pour modéliser le processus Natech, identifier 3 zones de fragilité, définir 5 scénarios de choc. Puis, en considérant que la résilience globale d’un territoire dépend notamment de la résilience des acteurs qui le constituent proposer un outil d’audit des parties prenantes du territoire afin d’estimer la résilience de chacun d’entre eux, les pistes de progrès et, in fine, améliorer la résilience du territoire qui les héberge. Mots-clefs : Natech, Science du danger, arbres de défaillance, aide à la décision, résilience territoriale.

Process-driven seafloor habitat sensitivity (PDS) has been defined from the method developed by Kostylev and Hannah (2007), which takes into account physical disturbances and food availability as structuring factors for benthic communities. It is a conceptual model, relating species’ life history traits to environmental properties. Physical environment maps have been converted into a map of benthic habitat types, each supporting species communities with specific sensitivity to human pressures. It is based on two axes of selected environmental forces. The "Disturbance" (Dist) axis reflects the magnitude of change (destruction) of habitats (i.e. the stability through time of habitats), only due to natural processes influencing the seabed and which are responsible for the selection of life history traits. The "Scope for Growth" (SfG) axis takes into account environmental stresses inducing a physiological cost to organisms and limiting their growth and reproduction potential. This axis estimates the remaining energy available for growth and reproduction of a species (the energy spent on adapting itself to the environment being already taken into account). It can be related to the metabolic theory of the ecology. The process-driven sensitivity (PDS) can be seen as a risk map that combines the two previous axes and reflects the main ecological characteristics of the benthic habitats regarding natural processes. Areas with low disturbance are areas with a naturally low reworking of the sediment, allowing the establishment of a rich sessile epifauna community, with K-strategy species. Areas with low SfG means that the environmental factors, even though there are not limiting, are in lower values, i.e. that it imposes a cost for species to live. In areas combining low disturbance and low SfG, big suspension-feeder species with long life and slow growth can often be found: these species are more vulnerable in case of added disturbance.

This dataset presents the resulting assessment grid (based on the EEA reference grid) with the classification of chemical status of the transitional, coastal and marine waters in the context of the Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD). This classification has been performed using the CHASE+ tool, with classifications of the matrices ‘water’, ‘sediment’ and ‘biota’ and indicators of ‘biological effects’, as well as an integrated classification of chemical status, combining results of all matrices. The chemical status is evaluated in five classes, where NPAhigh and NPAgood are recognised as ‘non-problem areas’ and PAmoderate, PApoor and PAbad are recognised as ‘problem areas’. The overall area of interest used is based on the marine regions and subregions under the Marine Strategy Framework Directive. Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that within the North East Atlantic region only the subregions within EEZ boundaries (~200 nm) have been included. This dataset underpins the findings and cartographic representations published in the report "Contaminants in Europe's Seas" (EEA, 2019): https://www.eea.europa.eu/publications/contaminants-in-europes-seas.

Shom is the national referent for the level of the sea in situ on all areas under French jurisdiction. In this capacity, he assures under the acronym REFMAR different coordination functions in the collection and dissemination of public data related to water level observations, in order to promote their use in multiple applications within the framework of international recommendations.

Process-driven seafloor habitat sensitivity (PDS) has been defined from the method developed by Kostylev and Hannah (2007), which takes into account physical disturbances and food availability as structuring factors for benthic communities. It is a conceptual model, relating species’ life history traits to environmental properties. Physical environment maps have been converted into a map of benthic habitat types, each supporting species communities with specific sensitivity to human pressures. It is based on two axes of selected environmental forces. The "Disturbance" (Dist) axis reflects the magnitude of change (destruction) of habitats (i.e. the stability through time of habitats), only due to natural processes influencing the seabed and which are responsible for the selection of life history traits. The "Scope for Growth" (SfG) axis takes into account environmental stresses inducing a physiological cost to organisms and limiting their growth and reproduction potential. This axis estimates the remaining energy available for growth and reproduction of a species (the energy spent on adapting itself to the environment being already taken into account). It can be related to the metabolic theory of the ecology. The process-driven sensitivity (PDS) can be seen as a risk map that combines the two previous axes and reflects the main ecological characteristics of the benthic habitats regarding natural processes. Areas with low disturbance are areas with a naturally low reworking of the sediment, allowing the establishment of a rich sessile epifauna community, with K-strategy species. Areas with low SfG means that the environmental factors, even though there are not limiting, are in lower values, i.e. that it imposes a cost for species to live. In areas combining low disturbance and low SfG, big suspension-feeder species with long life and slow growth can often be found: these species are more vulnerable in case of added disturbance.

Shom manages a network of permanent digital coastal tide-gauges on French coasts: the RONIM Sea Level Observation Network. Most tide-gauge observatories are partnered with one or more local partners.<br /><br /> Four main types of data are available for download:<br /> - “Raw high frequency" data: raw observations neither validated nor evaluated, obtained directly from the sensor. 1-minute measurement; integration time 15 seconds (on the minute); sampling period: 1 second.<br /><br /> - "Raw non-real time" data: raw observations neither validated nor evaluated, obtained directly from the sensor. 10-minute measurement; integration time 121 seconds (around every 10 minutes); sampling period: 1 second.<br /><br /> - "Validated non-real time" data: observations checked and validated by Shom from the "Raw non-real time" data. 10-minute measurement; integration time 121 seconds (around every 10 minutes); sampling period: 1 second.<br /><br /> - "Validated hourly" data: observations checked and validated by Shom, generated from "Validated non-real time" data. Hourly measurement obtained from the Vondrak filter with triangular weighting. The hourly height cannot be calculated in the event of an observation gap greater than 1.5 hours.