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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 20072008 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 Rpackage 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

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.

Region: The counties AustAgder 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 Rpackage 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 Rpackage 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: 1013 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

Laminaria hyperborea kelp forest is modelled for the Troms coast of northern Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 431 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 20082009 by Akvaplanniva, the model was run in 2011 by NIVA. The distribution model is based on depth, slope(log), wave exposure and median current speed, wave exposure being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope 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 counties Oslo, Akershus, Buskerud, Østfold, Vestfold Number of field observations: 653 Field sampling years: 2008 Prevalence: 12% Presence / absences: 76/577 Method: BRT run in R with the Rpackage Dismo. Number of predictor variables: 20 Information about the predictor variables: DEM (25 m resolution), slope (two resolutions; based on a 12.5 and a 25 m DEM respectively), aspect (based on a 12.5 m DEM), 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 DEM); wave exposure, 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, mean surface and seafloor current speed, standard deviation of surface and seafloor current speed, and 90th percentile of surface and seafloor current speed, all current speed predictor variables with 200 m resolution, but resampled to 25 m. AUC internal: 0.9.

Region: Nordland county Number of field observations: 4331 Field sampling years: 2011, 2012 Prevalence: 13% Presence / absences: 503 / 3828 Method: BRT run in R with the Rpackage Dismo. (GAMs were also tested and gave similar distribution.) Number of predictor variables: 9 Information about the predictor variables: curvature at coarse and medium detailed spatial resolution with a 1025 and 525 m moving calculating window respectively, based on a 25 m resolution DEM; DEM, slope, and wave exposure (all at 25 m resolution, the predictor variables are described in Rinde et al. 2006); mean salinity, mean current speed, maximum temperature and mean temperature, all with a 800 m resolution, but resampled to 25 m. AUC internal: 0.99.

Laminaria hyperborea kelp forest is modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 1170 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 20072008 by the Norwegian Institute for Water Research (NIVA) and the Institute for Marine Research (IMR), the model was run in 2009 by NIVA. The distribution model is based on depth, slope, terrain curvature, wave exposure and median current speed, wave exposure being the far most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope, terrain curvature 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. Analyses showed that coverage (density of kelp defined as classes) increased with predicted probability. 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.

The loss of Saccharina latissima kelp is modelled for the Skagerrak coast of Norway (Bekkby and Moy 2011). The model was carried out in the projection UTM zone 33N. The model shows the probability of sugar kelp loss. The model was developed based on 107 data points of absences and presences collected by NIVA. GAM analysis was carried out (presence being kelp loss, i.e. stations at which kelp is confirmed to be lost). The model was run in 2010 by NIVA. The kelp loss model is based on wave exposure and light exposure index (Bekkby and Moy 2010). The light exposure model had a spatial (horizontal) distribution of 12.5 m, the wave exposure model was resampled from 25 m resolution. The output model has a spatial (horizontal) distribution of 12.5 m. The work was part of the Coastal Monitoring Program, funded by the Norwegian Environment Agency.