Collections of munnopsid isopods of the BIOICE (Benthic Invertebrates of Icelandic Waters; 1991–2004) and the IceAGE1 (Icelandic Marine Animals: Genetics and Ecology; since 2011) expeditions included ten species of the genus Eurycope G.O. Sars, 1864, thereof are two species new to science. Thus, the descriptions of the two new species are presented herein. Eurycope elianae sp. n. is distinguished from the other species of the genus mainly by two long, slightly robust, simple setae on the tip of the rostrum in combination with the size and shape of the rostrum itself. E elianae sp. n. shares the presence of two long, slightly robust, simple seta on the tip of the rostrum with E. tumidicarpus . The shape of the rostrum itself is more similar to E. inermis and species of the E. complanata complex. E. aculeata sp. n. is characterized by possessing dorsomedial acute projections on pereonites 5–7, which is unusual for the genus. E. aculeata sp. n. is most similar to E. cornuta . Both new species are, so far, known only from localities south of the Greenland−Scotland Ridge.

Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity−Temperature−Depth (CTD) casts were conducted to collect hydrographic data and multicorer casts were conducted to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional scale. Two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over− and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future.