Comparison of three methods for identification of redfish ( Sebastes mentella and S. norvegicus ) from the Greenland east coast

• Published in: Fisheries Research
• Main Authors: Christensen, Helle Torp, Riget, Frank, Backea, Marie Balslev, Saha, Atal, Johansen, Torild, Hedeholm, Rasmus B.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2018
• Subjects: Species separation; Fisheries management; Otolith; Shape analysis; Microsatellites; OTOLITH SHAPE-ANALYSIS; NORTH-ATLANTIC; STOCK; MARINUS; DISCRIMINATION; SEPARATION; SIZE; COD; AGE; Greenland; Atlantic redfish; East Greenland; North Atlantic; Sebastes mentella; Sebastes norvegicus
• Online Access: https://pure.au.dk/portal/en/publications/c49f9159-e0b3-4cfe-bfec-5011cfeb3353; https://doi.org/10.1016/j.fishres.2018.01.003

In management of fisheries, knowledge about the species is crucial due to differences in life history traits. The first step is therefore species identification. For many species this task is straightforward, however for some species e.g. the two species of Atlantic redfish Sebastes mentella and Sebastes norvegicus, it can be difficult for an untrained eye. With the goal to separate the two species, we analysed otolith shape variation of S. mentella and S. norvegicus caught on the continental slope of East Greenland using the R package "Shape R". Results were evaluated against genetic analysis of the same fish, and compared to results of both a visual identification of the two species and a separation based on a linear discriminant analysis on standardised values of fish length, fish weight and otolith weight. It was concluded that the objective otolith shape analysis using the Shape R package analysis achieve a reasonable classification success, however with a clear bias towards S. mentella. Classification using the otolith weight achieved a slightly higher success than the shape analysis making it a promising method. Furthermore, the method is at the same time both objective, less time consuming than the otolith shape analysis and less expensive than genetic analysis. However, the visual classification method of the whole fish had the highest success rate of the three tested methods, which despite the need for trained technicians makes it the most successful method.