Bathymétrie du canyon de Blackmud au canyon de Douarnenez (projet CoralFISH), au pas de 50 mètres.Cette résolution spatiale delagrille,à50 mètres,a été réalisée dans le cadre du projet CoralFISHpour l'édition des cartes "Sur les traces des coraux d'eau froide du golfe de Gascogne, 8Cartes bathymétriques et géomorphologiques".Les données sont issues des campagnes:- BOBGEO1 (2009) : N/O Pourquoi Pas ?,http…

Bathymétrie - Zaire (ZAIANGO 1 et 2, ZAIANGOCAR, ZAIANGO_OBS_BIOL, REPREZAI LEG1, WACS, CONGOLOBE), au pas de 100mètres (projection WGS84/Mercator 5°30' S). Compilation de données bathymétriques de plusieurs campagnes océanographiques sur l'éventail turbiditique du Zaire. Description campagnes: ZAIANGO1: https://doi.org/10.17600/98010100 ZAIANGO2: https://doi.org/10.17600/98010110 ZAIANGOCAR: https://doi.org/10.17600/20050 ZAIANGO OBS…

Bathymétrie - Guyane, marge guyanaise et plateau de Démérara (synthèse, 2019), au pas de 250 mètres.Ce MNT est une compilation de données issues des campagnes GUYAPLAC, LOBESTORY, et CITHER 1. Le MNT a été produit dans le cadre du programme EXTRAPLAC pour la délimitation du Plateau Continental au-delà des 200 M. Description campagnes…

Sediment average grain size in French Mediterranean waters was generated from sediment categories. This rough granulometry estimate may be used for habitat models at meso- and large scale.

Seabed shear stress (in N.m-2) is a measure of the friction of water on the seabed due to waves and currents. The 90th percentile over the available period is used as layer for habitat models prediction.

Le MNT Bathymétrique à haute résolution (40 cm) des plateformes récifales de La Réunion est extrapolé du traitement de la colonne d'eau appliqué aux images hyperspectrales acquises en Mai 2015 dans le cadre du projet HYSCORES (Ifremer/UBO/Office de l'eau Réunion). Les secteurs non traités à partir des données hyperspectrales ont été complétés par le MNT Litto3D (2009-2010) pour proposer une couverture continue de l'ensemble des…

Abundance of several species in coastal nurseries along french coasts. Kriging error (v) resulting from the interpolation of these data was maped.

Biological data have been expressed in abundance (number of individuals per 20 m3). Kriging error (v) resulting from the interpolation of these data was maped.

Modelised abundance of species or prediction uncertainty.

Species habitat occupation, observed during the first quarter IBTS from 1980 to 2010, has been expressed as rare, occasional, frequent, recurrent depending on density levels and variability over time.

Abundance of several species in coastal nurseries along french coasts.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation. Abundance mean (mean) and standard deviation (st) have been calculated.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation. Abundance mean (mean) and standard deviation (st) have been calculated. The sum of the kriging error for each yearly kriged abundance fits to the kriging error (v).

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Abundances were recoded in term of presence-absence.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.