Original Paper

Marine Biodiversity

, Volume 39, Issue 4, pp 291-302

First online:

Mapping the benthos: spatial patterns of seabed-dwelling megafauna in a Swedish Fjord, as derived from opportunistic video data

  • Genoveva Gonzalez-MirelisAffiliated withDepartment of Marine Ecology, Tjärnö, University of Gothenburg Email author 
  • , Per BergströmAffiliated withDepartment of Marine Ecology, Tjärnö, University of Gothenburg
  • , Tomas LundälvAffiliated withSven Lovén Centre for Marine Sciences, Tjärnö, University of Gothenburg
  • , Lisbeth JonssonAffiliated withDepartment of Marine Ecology, Tjärnö, University of Gothenburg
  • , Mats LindegarthAffiliated withDepartment of Marine Ecology, Tjärnö, University of Gothenburg

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It is widely acknowledged that mapping of benthic diversity is needed to aid in the management and conservation of marine ecosystems, but the choice of scale is contingent upon the patterns of spatial structure inherent to the benthos, which are often unknown. In this paper, spatial autocorrelation analysis is used to detect and describe fine-scale patterns of spatial structure in assemblages of epibenthic megafauna of the seabed below 20 m depth at the Koster fjord/archipelago area (Sweden). Presence/absence of benthic organisms was obtained from video images, which had been collected by means of a remotely operated vehicle. For sample sizes (grain) of <1 m2, and maximum between-sample distance (lag) of 200 m, rank-correlograms revealed the presence of patches in all 12 sites surveyed, and faunal homogeneity (positive spatial autocorrelation) was always detected within distances <20 m, though there was variation across sites in the sizes of patches. These findings were further used to resample the data at coarser grain sizes, to enable exploring of the faunal composition of the patches. We conclude that spatial autocorrelation analysis can greatly improve the design of sampling schemes by ensuring parsimoniousness, and maximizing the chances of detecting patterns. The procedure shown here is especially well suited to carry out subsequent mapping because it can readily discriminate between types of biotopes.


Spatial structure Spatial autocorrelation analysis Epibenthic megafauna Underwater video Mapping