Summary: | The aim of this work is to improve the knowledge of the mollusc fauna of Terra Nova Bay (Ross Sea), on the basis of more than 100 stations sampled from 25 to 1100 m depth, during Italian Ant- arctic Expeditions (austral summers 1987/1988, 1989/ 1990, 1993/1994, 1994/1995, 1995/1996 and 1997/1998). In shallow waters, gastropod fauna is represented by 31 species, among which Neobuccinum eatoni, Onoba gelida, Powellisetia deserta, Philine cf. apertissima and Austro- doris kerguelenensis are the most abundant, while among bivalves (25 species) the commonest species are Adam- ussium colbecki, Yoldia eightsi, Laternula elliptica and ``Montacuta'' nimrodiana. At present, most of the Ant- arctic mollusc species are known as having circumant- arctic distribution and are considered eurybathic. Moreover, in general, only scarce data are available on their substrate preferences, because of their occurrence in scattered sampling stations with diā¬erent sediment features. In the present study carried out at Terra Nova Bay, based on a relatively high number of stations, a clear zonation of mollusc assemblages is shown, ac- cording to depth and sediment features. While gastro- pods characterise the superRcial algal belts dominated by the red algae Iridaea cordata and Phyllophora antarctica, bivalve distribution is wider. Adamussium colbecki is the dominant species in the upper 100 m, both on soft and hard bottoms, if the slope is suitable (density up to 40 }60 ind./m2). On coarse sands, this species is frequently accompanied by L. elliptica (density <20 ind./m2), while Y. eightsi characterises organic- enriched bottoms (density 70 }80 ind./m2). Below 200 m depth Adacnarca nitens, Limatula hodgsoni, ``Monta- cuta'' nimrodiana and Mysella gibbosa are commonly found on relatively coarse sediments, while Thyasira dearborni and Yoldiella ecaudata prefer muddy bottoms. In order to identify bivalve assemblages, multivariate analyses were applied to stations grouped into depth ranges, sediment features and location. While shallow ...
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