Sussex coastal habitats inshore pilot II: Marine habitat and bathymetry modelling

The Sussex Coastal Habitats Inshore Pilot (SCHIP) 2 project follows on from the preceding SCHIP 1 project, which was led by the Sussex Inshore Fisheries and Conservation Authority (IFCA), working in partnership with Sussex Wildlife Trust, and funded by the Environment Agency. The project aims to explore and analyse existing sea floor habitat and bathymetric data sets available for the Sussex IFCA district. These data are used to construct both a broad scale habitat model classified to European Nature Information System (EUNIS) level 3 and a detailed fine scale habitat model classified up to EUNIS level 6. In addition, a bathymetric model of the entire district is produced from survey data taken by IFCA’s patrol and research vessel ‘Watchful’. Four existing seabed habitat data sets (EUNIS Sussex 2010, JNCC UK Sea Map 2010, EUNIS South East, and RoxAnn) were converted into a single layer containing the features and attributes of all four individual shape files. EUNIS habitat values from all four polygon data sets were compared using a four way comparison of column values, and returned the number of matching values. This was carried out to compare all four data sets, and to provide a final layer which showed the level of agreement between layers and described the extent of known seabed habitats to EUNIS level 3. In order to create a finer-scale and continuous EUNIS habitat layer a Voronoi polygon method was developed and used. Voronoi polygons are presented as an independent method by which to convert point data into a polygon coverage and to divide up the seabed into EUNIS habitat codes. Within each voronoi polygon, any given location contained is closer to the known point in that polygon than any other known point (ESRI, 2007). All non-sampled locations are classified in accordance with the nearest known point Where survey point density is higher (more known points per unit area), smaller polygons are constructed and the data can show increased habitat variability. Areas where voronoi polygons are smaller are likely to represent seabed habitats more closely due to higher survey/point density. The predictive habitat maps suggest that A5 (sublittoral sediment) habitats are dominant throughout the district. A3 (Infralittoral rock and other hard substrata) and A4 (Circalittoral rock and other hard substrata) habitats are predicted in small areas. The EUNIS level 3 map is perhaps adequate as an indication of the overall habitat trends within this biologically significant region. However, the method has been applied up to EUNIS level 4, 5, and 6 at specified locations, where data are available, and facilitates more detailed biological interpretation at specific locations. Fine-scale seabed habitat models were validated in sample areas against contemporary MBES surveys and habitat maps to assess accuracy; validation suggested a strong associated with independent data. The study highlights the importance of citizen science data (e.g. Seasearch) in developing an increased understanding and knowledge of the habitats in this region and others throughout the UK. More developed seabed habitat maps are crucial for the success of current and future fisheries management.