Sårbare marine biotoper – modellert utbredelse Svampspikelbunn

Definition: Sponge-spicule bottom distribution model. This raster dataset represents density of megafaunal (sponge) taxa Aplysilla sulfurea, Geodia spp., Steletta sp., and Stryphnus ponderosus. Units are numbers of individual animals per 100 m2 (n/100m2). When occurring at high density these taxa combined are assumed to indicate the presence of what we dub as a “sponge spicule bottom”: a type of deep-sea sponge aggregation mostly occurring on soft substrata featuring a characteristic, often thick mat of shed sponge spicules, and dominated by sponges in suborder Astrophorina. This type of habitat is known as “ostur” in some North East Atlantic countries. The habitat definition used here has been refined from the definition provided by OSPAR of “Deep-sea sponge aggregations” (OSPAR 2010), which is commonly tagged as a type of Vulnerable Marine Ecosystem, and the modelled sponges are considered VME indicators. The model is very good as a classificatory model (AUC = 0.8, p-value ~ 0), and it explains the distribution of density relatively well (variance explained by cross-validation = 11 %). Approximately 1 % of the area is linked to a field observation, and 99 % of it are model predictions. This dataset was commissioned by the Norwegian Environment Agency and the Directorate of Fisheries to aid in the revision of the Barents Sea Management Plan.

Updated: 25.05.2024
Owner: Institute of Marine Research


Datasettet kan brukes som et verktøy for marin areal- og miljøplanlegging, sårbarhetsanalyser, habitatskartlegging og i forbindelse med installasjoner på havbunnen og aktiviteter som kan ha påvirkning på havbunnen.

Explanation of assessment of FAIR principles:

There have made various tests to evaluate datasets in relation to the FAIR criteria. These are our interpretations, which then assess the criteria in relation to standards and protocols used for spatial data in Norway and Europe. For more information on which calculations we use for each indicator, look at the details of FAIR assessments for each specific dataset.

FAIR-status: 92%

Mareano status