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Big data approaches reveal large-scale spatial patterns in marine epifauna
Cooper, K.M.; Curtis, M.; Downie, A.-L.; Bolam, S.G. (2026). Big data approaches reveal large-scale spatial patterns in marine epifauna. ICES J. Mar. Sci./J. Cons. int. Explor. Mer 83(1): fsaf227. https://dx.doi.org/10.1093/icesjms/fsaf227
In: ICES Journal of Marine Science. Academic Press: London. ISSN 1054-3139; e-ISSN 1095-9289, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Trefwoord
    Clustering
Author keywords
    epifauna, 2 m beam trawl, random forest, spatial maps

Auteurs  Top 
  • Cooper, K.M., meer
  • Curtis, M.
  • Downie, A.-L.
  • Bolam, S.G.

Abstract
    Comprehensive maps of biological characteristics are increasingly employed to support the management of human activities in marine environments. However, their development is often constrained by insufficient data coverage across broad spatial scales. Here, we apply a big data approach by integrating marine epifaunal data from 2 m beam trawls across the UK shelf and wider North Sea to enhance understanding of the ecological characteristics of epifaunal assemblages in sedimentary habitats. We analyse spatial patterns in univariate metrics (taxon richness, total abundance) and assemblage taxonomic structure. Taxon richness was found to peak in the northern North Sea and English Channel, while abundance hotspots occurred along the Norwegian coast, southern North Sea, and inshore regions around the UK and Ireland. We also identify 11 distinct assemblage types, each exhibiting their own taxonomic epifaunal composition, with strong biogeographic structure. We identify the main environmental drivers shaping these patterns and discuss how the creation of such maps, and the added insight gained from them, may be used to facilitate the management of anthropogenic activities.

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