Dataset

Taxonomy of Famelica, Glaciotomella and Rimosodaphnella (Conoidea: Raphitomidae) from the deep sea of temperate Australia.

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The samples studied herein were selected among all Raphitomidae ethanol-preserved material from the malacological collections of the Australian Museum, Sydney (AMS), the South Australian Museum, Adelaide (SAMA) and the Muséum national d'Histoire naturelle, Paris (MNHN). All studied material has been collected off Australia during the expeditions IN2015_C01, IN2015_C02 (GAB) and IN2017_V03 (Tasman and Coral Seas), targeting (among other sites) a number of Commonwealth Marine Reserves (CMR), and other localities (mainly of the tropical and temperate Indian and Pacific Oceans), during a number of voyages that formed part of the Tropical Deep-sea Benthos programme of MNHN. The data has been acquired on the following RV Investigator voyage(s) IN2015_C01 IN2015_C02 IN2017_C01 and IN2017_V03 with links to additional metadata, datasets, events, deployments, publications etc. If this data has been used in any products, please acknowledge with the following: We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353) in undertaking this research.

Citation: Criscione Francesco, Hallan Anders, Puillandre Nicolas, Fedosov Alexander (2021) Snails in depth: integrative taxonomy of Famelica, Glaciotomella and Rimosodaphnella (Conoidea: Raphitomidae) from the deep sea of temperate Australia. Invertebrate Systematics 35, 940-962. https://doi.org/10.1071/IS21008

Published: February 14, 2025 at 09:52

License: This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License

URL: https://www.marine.csiro.au/ipt/resource?r=am_raphitomidae

Contacts:

Francesco Criscione
Australian Museum

24
occurrence records
24
measurements and facts
11
taxa
9
species

Taxa

Missing and invalid fields

No missing or invalid fields.

Quality flags

The OBIS data quality flags are documented at https://github.com/iobis/obis-qc.

No quality flags.

Measurement types

DNA derived data