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The first bay-wide synoptic survey of benthic habitat quality in Narragansett Bay, Rhode Island, USA, was conducted in August of 1988. Twenty years later, we revisited the same sampling locations as the original survey using similar sediment profile imagery technology and analysis tools. Like estuaries throughout the US, increased temperatures and reductions to anthropogenic nutrient inputs have cumulatively affected Narragansett Bay in the intervening 20 years. To understand how these changes may have influenced benthic organic enrichment and habitat quality, we compared the prevalence and spatial arrangement of benthic biotopes (i.e., biotic and abiotic benthic descriptions) between 1988 and 2008 surveys. Biotopes dominated by Ampelisca spp. tubiculous amphipods increased >fivefold between 1988 and 2008, and expanded into the more urban, anthropogenically stressed Providence River estuary. Ampelisca beds occurred at critical boundaries in organic enrichment and habitat quality in both years and indicated the quantity of organic matter reaching the benthos. In general, benthic biotopes reflect the degree of benthic-pelagic coupling and are an important link between estuarine water quality and other marine life. As estuaries globally cope with the effects of increased warming and legislated anthropogenic nutrient reductions, rapid assessments of benthic biotopes will be critical for understanding changes to whole-estuary condition as a result of these cumulative stressors.

Within the perspective of siting wind farms offshore of Rhode Island, USA, the State and National Environmental Agencies had requested a local marine ecological assessment, which led to an ecological zoning of the area. In view of expanding this zoning outside its limit of the test area and filling gaps in ecological zones, an effort to model those ecological zones using marine landscape or abiotic features was carried out. This study tests the accuracy of selected machine learning algorithmic models, decision tree, and random forest, for relating marine landscapes features to ecological sub-regions. Both models show to be good predictive tools with accuracy after cross validation of the order of 5–3%. Key abiotic variables to provide an accurate model were investigated. The study demonstrates the importance of the distance to coast, the sediment characteristics (fraction of clay, median size of the sediments), the hydrodynamic features, in particular not only tidal current/drag force, but also wave drag force, and finally the oceanographic characteristics such as stratification and sea surface temperature to built a good predictive model. Those findings provide some insight on the pre-monitoring effort optimization.

An important first step in marine spatial planning and ecosystem-based management efforts is the creation of benthic habitat maps that allow scientists and managers to understand the distribution of living and non-living resources on the seafloor. However, the location of boundaries between and composition of habitats is highly dependent on the approach taken to integrate abiotic and biotic information. The purpose of this study was to test “top-down” and “bottom-up” approaches for integrating physical and biological data derived from commonly used sub-tidal benthic mapping tools to create a habitat map compatible with the US Coastal and Marine Ecological Classification Standard (CMECS). We found that a top-down framework, where we tested for differences in macrofauna assemblages among side scan sonar facies, defined two broad-scale and general habitats. Using the bottom-up approach, where patterns in abiotic and biotic variables were examined with multivariate statistics (BEST, LINKTREE, ANOSIM, SIMPER), we generated seven biotopes based on the macrofauna abundance, percent sand, water depth, and backscatter standard deviation that corresponded well to, but provided more fine-scale detail than the top-down habitats. We were able to use the statistical relationship between abiotic variables and macrofauna assemblages in the LINKTREE to predict the spatial distribution of assemblages over ∼50% of the study area. We created a local catalogue of biotopes specific to our study area that contributes to the CMECS library. In addition, we were able to fully map CMECS Geoform, Surface Geology, and Biotic Cover Components. This mapping effort represented real progress toward reconciling the “data density mismatch” between physical and biological mapping methods, and it provided further evidence that using a bottom-up methodology preserves species–environment relationships.

The EUNIS (European Union Nature Information System) habitat classification system aims to provide a common European reference set of habitat types within a hierarchical classification, and to cover all terrestrial, freshwater and marine habitats of Europe. The classification facilitates reporting of habitat data in a comparable manner, for use in nature conservation (e.g. inventories, monitoring and assessments), habitat mapping and environmental management. For the marine environment the importance of a univocal habitat classification system is confirmed by the fact that many European initiatives, aimed at marine mapping, assessment and reporting, are increasingly using EUNIS habitat categories and respective codes. For this reason substantial efforts have been made to include information on marine benthic habitats from different regions, aiming to provide a comprehensive geographical coverage of European seas. However, there still remain many concerns on its applicability as only a small fraction of Europe’s seas are fully mapped and increasing knowledge and application raise further issues to be resolved. This paper presents an overview of the main discussion and conclusions of a workshop, organised by the MeshAtlantic project, focusing upon the experience in using the EUNIS habitats classification across different countries and seas, together with case studies. The aims of the meeting were to: (i) bring together scientists with experience in the use of the EUNIS marine classification and representatives from the European Environment Agency (EEA); (ii) agree on enhancements to EUNIS that ensure an improved representation of the European marine habitats; and (iii) establish practices that make marine habitat maps produced by scientists more consistent with the needs of managers and decision-makers. During the workshop challenges for the future development of EUNIS were identified, which have been classified into five categories: (1) structure and hierarchy; (2) biology; (3) terminology; (4) mapping; and (5) future development. The workshop ended with a declaration from the attendees, with recommendations to the EEA and European Topic Centre on Biological Diversity, to take into account the outputs of the workshop, which identify weaknesses in the current classification and include proposals for its modification, and to devise a process to further develop the marine component of the EUNIS habitat classification., Sí