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Carbonate sand deposits are modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Carbonate sand deposits were defined as having at least 50 % carbonate content. The model was developed based on 1105 data points and GAM analyses of presence and absence points. Data were collected 2007-2008 by the Geological Survey of Norway (NGU), the model was run in 2009 by NIVA. The distribution model is based on depth, wave exposure and maximum current speed, depth being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator.
Region: The county Hordaland Number of field observations: 637 Field sampling year: 2004, 2005,2009, 2010 Prevalence: 34% Presence / absences: 215/422 Method: BRT run with the R-package Dismo. Number of predictor variables: 23 Information about the predictor variables: DEM (25 m resolution), slope, aspect, curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m resolution DEM); wave exposure, latitude, longitude, and optimal radiation index, all with 25 m resolution; and maximum surface and seafloor current speed, slope of maximum surface and seafloor current speed, minimum surface and seafloor current speed, standard deviation of seafloor current speed, 10 and 90th percentile of surface and seafloor current speed, all current speed predictor variables with 200 m resolution, but resampled to 25 m. AUC independent data: 0.88
An important task undertaken this year was to define sub-regional areas of the ICES greater North Sea eco-region. The sub-regional areas correspond to meaningful eco-logical units whose boundaries are defined by strong gradients in their physical oceanography, such as changes in depth, sediment transport, salinity, oxygen and currents. The four sub-regions of the ICES greater North Sea eco-region are; i. North-ern North Sea, ii. Southern North Sea, iii. Skagerrak and Kattegat, and iv. English Channel
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Cette carte des peuplements benthiques subtidaux du secteur Trégor-Goëlo est le résultat du traitement, de l'analyse et de l'agrégation des données des campagnes REBENT 10 et 11 (2006), HALIOTREGOR (2008) et IFR-NEOMYSIS (2011). Deux sources croisées d'acquisition des données ont été employées : un système acoustique embarqué (sondeur multifaisceaux) et remorqué (sonar à balayage latéral), permettant de définir des premières classes de signatures acoustiques, correspondant à des unités morpho-sédimentaires, complétées par des campagnes de prélèvements à la benne (échantillons sédimentaires biologiques) et des profils vidéo.
Cartography of benthic communities to promoting adequate strategies for the use, management and conservation of littoral areas depending on the ecological value of the different benthic communities established and on the local geographical distribution.
Laminaria hyperborea kelp is modelled for the Sandoy area at the More and Romsdal coast on the West coast of Norway (Bekkby et al. 2009). The model was carried out in the projection UTM zone 32N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 384 data points and GAM analyses of presence and absence points (presence being kelp regardless of density). Data were collected 2008 and the model was run by NIVA in 2009. The distribution model is based on depth, slope, terrain curvature, wave exposure and a light exposure index (Bekkby et al. 2009), depth being the most important variable, followed by terrain curvature and wave exposure (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope, terrain curvature, wave exposure and light exposure index models had a spatial (horizontal) distribution of 10 m, the current speed model was resampled from 25 m resolution. The output model has a spatial (horizontal) distribution of 10 m. Analyses showed that coverage (density of kelp defined as classes) increased with predicted probability. The work was funded by the Research Council of Norway.
Raster showing the average diffuse attenuation coefficient of photosynthetic active radiation (KDPAR) between 2005 and 2009, values in metres^-1. Data was collected by the MERIS satellite and this layer was created for use in the 2019 EUSeaMap. In 2018 the coverage was extended to Iceland and Barents Sea. Dataset's spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic. The spatial resolution of the dataset is around 100m, but that of the original data source is around 250m. Created by the EMODnet Seabed Habitats consortium using data from the European Space Agency MERIS instrument.
Region: The counties Aust-Agder and Telemark Number of field observations: 1848 Field sampling years: 1992 and 2008 Prevalence: 13.6% Presence / absences: 252/1596 Method: BRT run in R with the R-package Dismo. (The model predicts carbonate sand on peaks and in sheltered areas where it is unlikely to find carbonate sand. Such areas have been identified through rules set up in collaboration with geologists at Geological Survey of Norway (NGU; curvature index values >|1|, and the wave exposure index less than 10 000, cf Rinde et al. 2006) and removed from the shape layers for predicted presence of carbonate sand created from the grid layer. Number of predictor variables: 10 Information about the predictor variables: curvature at coarse, medium and detailed resolution with a 1025, 525 and 125 m moving calculating window respectively, based on a 25 m resolution DEM; DEM, slope at two resolutions (12.5 and 25 m); wave exposure, longitude, and an optimal radiation index, all with 25 m resolution; and aspect based on a 12.5 resolution DEM. AUC internal: 0.96.
Region: the county Rogaland Number of field observations: 633 Field sampling year: 1992, 1993, 2012 Prevalence: 43 and 22 for 50% and 85% calcium carbonate content respectively Presence / absences: 275/358 and 142/491 for 50% and 85% calcium carbonate content respectively Method: BRT run with the R-package Dismo. The grid is made up by a combination of three BRT models; two simplified models including 12 and 13 predictors for carbonate sand with 50% calcium content) and one BRT model based on 10 predictor variables for carbonate sand with 85% calcium content; all run with tree complexity equal to 5. The grid constitutes the maximum predicted probability values among these three models, which all are “smoothed” by applying a neighborhood analysis (i.e. mean of 3x3 neighbor cells) in advance of combining the models). Number of predictor variables: 10-13 Information about the predictor variables: depth, curvature at detailed, medium and coarse resolution (i.e. applying a 100, 500 and 1500 m moving calculating window respectively, based on the 25 m resolution DEM), wave exposure, slope of wave exposure, and optimal radiation index, all with 25 m resolution; minimum and average seafloor temperature, average seafloor salinity, minimum and average seafloor current speed, and slope of average seafloor current speed, the latter predictors from a hydrodynamic model with 800 m spatial resolution. AUC internal: 0.98