Tracing the geographic origin of Atlantic cod products using stable isotope analysis

Increasing demand for fish and seafood means that the traceability of marine products is becoming ever more important for consumers, producers and regulators. Highly complex and globalised supply networks create challenges for verifying a stated catch region. Atlantic cod is one of the most commercially important species in the northeast Atlantic. Several regional fisheries supply cod into the trade network, of which some are at greater risk of overexploitation than others. Tools allowing retrospective testing of spatial origin would significantly assist sustainable harvesting of fish, reducing incentives for illegal fishing and fraud.

Here, we investigate whether stable isotope ratios of carbon, nitrogen and sulphur can be used to retrospectively identify the catch region of Atlantic cod (Gadus morhua). We measured the isotopic composition of muscle tissue from 377 cod from 10 catch regions across the northeast Atlantic and then applied three different assignment methods to classify cod by region of most likely origin. The assignment method developed was subsequently tested using independently sourced, known-origin samples.

Individual cod could be traced back to their true origin with an average assignment accuracy of 70-79% and over 90% accuracy for certain regions. Assignment success rates comparable to those using genetic techniques were achieved when assigning among restricted and pre-selected regions. However, assignment accuracy to the fishery region estimated from independent samples across the whole geographic range of cod averaged ~25% overall, highlighting the need for careful application of isotope-based approaches.

Stable isotope techniques can provide effective tools to test for origin in Atlantic cod, but not all catch regions are isotopically distinct. Stable isotopes could be combined with genetic techniques to result in higher assignment accuracy than could be achieved using either method independently. Assignment potential can be estimated from reference datasets, but estimates of realistic assignment accuracy require independently collected data.