Type of resources
Available actions
Topics
Keywords
Contact for the resource
Provided by
Formats
Representation types
Update frequencies
status
Scale

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: 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.

Region: The counties AustAgder and Telemark Number of field observations: 1707 Field sampling years: 20052008 Prevalence: 43% Presence / absences: 742/965 Method: GAM analysis in the statistical program language R, using "look up tables" and the "Grasp Extension" to transfer the predictions to ArcView GIS Number of predictor variables: 6 Information about the predictor variables: curvature at coarse and detailed resolution (i.e. applying a 1025 and 125 m moving calculating window respectively) on a DEM with 25 m resolution; DEM, wave exposure, longitude and an optimal radiation index, all at 25 m resolution. AUC internal: 0.88