Habitat Suitability Model for Zostera marina beds in the UK: 2020

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Zostera marina beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 30 0m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed PAR at the Seabed Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Mean of annual minima temperature at the seabed (over 30-year period) Absolute minimum of seasonal salinity