Extrait de l'Atlas Aquitaine, Limousin et Poitou-Charentes sur la filière forêt-bois

PPRIF

Ce jeu de données représente sous forme de polygones, les espaces situés à moins de 200m de terrains en nature de bois et forêt, appelés "zones exposées au risque feu de forêt", dont les paramètres sont précisés par l'article R133-5 du Code forestier. Ces zones sensibles au risque d'incendie de forêt comprennent les formations forestières ainsi que la zone périphérique de 200m de large les entourant. Sur cet ensemble s'applique une règlementation particulière afin d'y réduire le risque. Notamment concernant l'obligation légale de débroussaillement qui s'applique dans ces espaces, cette cartographie est annexée aux plan locaux d'urbanisme ou aux documents d'urbanisme en tenant lieu (article L134-15 du Code forestier).

The dataset presents the potential combined effects of land-based pressures on marine species and habitats estimated using the method for assessment of cumulative effects, for the entire suite of pressures and a selected set of marine species groups and habitats by an index (Halpern et al. 2008). The spatial assessment of combined effects of multiple pressures informs of the risks of human activities on the marine ecosystem health. The methodology builds on the spatial layers of pressures and ecosystem components and on an estimate of ecosystem sensitivity through an expert questionnaire. The raster dataset consists of a division of the Europe's seas in 10km and 100 km grid cells, which values represents the combined effects index values for pressures caused by land-based human activities. The relative values indicate areas where the pressures potentially affect the marine ecosystem. This dataset underpins the findings and cartographic representations published in the report "Marine Messages" (EEA, 2020).

Whole genome pooled sequencing of individuals from 4 populations and 3 different color phenotype in order to uncover the genetic variants linked to color expression in the pearl oyster P. margaritifera.

Rapport final ONF

scRNA-seq reads from a Pacific oyster (Crassostrea gigas) hemocyte preparation. Hemocytes were isolated from a unique immunologically naive animal (Ifremer Standardized Animal, 18 months) and single-cell drop-seq technology was applied to 3,000 individual hemocytes.

Individuals from five populations of Sabellaria alveolata (Scotland, France-Saint Michel Bay, France-Biarritz, Portugal, and Morocco) were maintained under common garden conditions to investigate the gene expression response to temperature stress in the honeycomb worm. Worms were exposed to five sequential temperature treatments, and whole-organism samples were collected for RNA-Seq analysis. Gene expression patterns were analyzed to assess thermal acclimation and adaptation across populations.

The dataset presents the potential combined effects of human activities and pressures on marine species and habitats estimated using the method for assessment of cumulative effects, for the entire suite of pressures and a selected set of marine species groups and habitats by an index (Halpern et al. 2008). The spatial assessment of combined effects of multiple pressures informs of the risks of human activities on the marine ecosystem health. The methodology builds on the spatial layers of pressures and ecosystem components and on an estimate of ecosystem sensitivity through an expert questionnaire. The raster dataset consists of a division of the Europe's seas in 10km and 100 km grid cells, which values represents the combined effects index values for pressures caused by human activities. The relative values indicate areas where the pressures potentially affect the marine ecosystem. This dataset underpins the findings and cartographic representations published in the report "Marine Messages" (EEA, 2020).

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.