Sampling strategies to evaluate the status of offshore soft sediment assemblages

Reliable descriptions of the status of offshore seabed habitats usually require substantial investment in field data collection and sample analysis. While assessment of, for example, biogenic reef habitat can often include simple physical parameters (e.g. spatial extent), comparative measures for soft sediment habitats generally rely on the distribution and relative abundance of species, with a description of the associated sedimentary environment. To investigate the power of surveys to detect significant trends in assemblage structure, samples of meiofauna, macroinfauna and megafauna (i.e. representing ecological components from nematodes to demersal fish), were collected from four offshore mud and sand habitat sites in western UK shelf seas during July 2004 and 2005. Spatial arrays of samples within these sites, up to 23 km apart, were designed to optimise descriptions of assemblage structure and the patterns of spatial distribution at a local scale. Analyses of species abundance, biomass and taxonomic relatedness of the species complement at each site suggested that most assemblages represented relatively unimpacted regional conditions. The power of the sampling programme to detect a significant change in univariate community attributes was assessed. The variability in many of the community attributes indicated that intensive replicate sampling would be required to detect ecologically important changes. Improving the power of such benthic surveys to detect trends would therefore require substantial additional time and effort to be invested in sample collection and analysis. Resource analysis showed that the time from gear deployment to complete sample identification was gear-dependent, lowest per sample for meiofauna (10h) and megafauna (6-12h), and highest for macroinfauna (12-22 h). These results have implications for the development of meaningful indicators of habitat status for offshore soft sediment habitats, and the resources required for effective monitoring of change.