transparent & evidence-based information on seafood fraud



Seafood Ethics is part of an ongoing effort to properly and transparently characterize seafood mislabeling, using available evidence and statistical modeling. First, we have compiled a global database of mislabeling studies. Second, we have documented the effort for which products have been tested for mislabeling (# of studies and # of samples tested). Third, we have developed statistical models to estimate mislabeling rates. Last, we have documented the substitute products involved in mislabeling. Mislabeling necessarily involves two products: we refer to them as the expected product (e.g., the species that the product is purported to be based on a label) and the substitute—the true identity of a mislabeled product.

seafood Mislabeling database

We maintain a global database of mislabeling studies. The database currently includes 141 studies published before December 2017: 117 peer-reviewed scientific papers and 24 publications from non-peer-reviewed sources. The latter includes publications by government agencies, media outlets, and non-profit organizations. Certain data is filtered from the database to remove potential biases, such as when sample sizes of products tested can not be determined or when only mislabeled samples of a product are reported and information on correctly labeled samples are omitted. The database includes over 27,000 samples. However, the sampling effort is highly skewed toward certain products and geographies.

Luque Fig2 02.png

gLOBAL EFFORT to document mislabeling

Seafood mislabeling has been evaluated in 38 countries. However, two-thirds of those countries include two or less studies. The United States (37 studies), Italy (24), and Spain (18) have the most studies, followed by Brazil (10) and the United Kingdom (10). Sampling effort is highly skewed toward certain products. For example, 57% of all tested products have been tested by a single study, and 50% have been tested with ≤5 samples across all studies. When effort is broken down by seafood product per study, average sample size is small. These low sample sizes currently make it challenging to estimate mislabeling rates for many products.

For each product, we provide the number of studies and the total number of samples tested for mislabeling, broken down to the country level. Thus, you can determine if a product of interest has been tested in your country, and if so, the number of samples those studies include.

Luque Fig4 excn.png

Mislabeling estimates for seafood products

We use statistical models to estimate mislabeling rates. Compared to simply taking the average of the number times a product has been mislabeled (# mislabeled samples divided by #total number of samples tested), our approach has important advantages for characterizing seafood mislabeling. First, it takes effort into account. This is important, especially when samples sizes are small. For example, is it not uncommon to flip a coin four times and get three tails. But, that does not mean the probability of getting a tail is 75%. Using the same analogy, if a study tests four Pacific Salmon samples for mislabeling and three turn out to be Atlantic Salmon—that does not mean the mislabeling rate for Pacific Salmon is 75%. If we flip a coin 400 times, we know that the probability of getting tails converges to 50%. Our statistical models act in a similar way, giving more weight to studies with more samples. Second, our statistical models not only estimate mislabeling rates—they estimate the uncertainty of those rates. That is, the precision of the estimate. Sticking with the coin toss analogy: precision is the degree to which you get the same results if you repeated a four (or 400) coin flip experiment multiple times under the same conditions. Not surprisingly, you are much more likely to get the same result repeating 400 coin flips compared to four coin flips.

For each product, we provide the most likely mislabeling rate given the current data, which statisticians refer to as the most credible value. We provide a minimum and maximum likely value as a measure of precision. These values can be interpreted with the following analogy: if you re-tested the same number of samples under the same conditions, there is 95% chance the mislabeling estimate would be between the minimum and maximum values. Thus, the greater the spread between the two values—the more uncertain the mislabeling rate, given the current data available. We also provide 2017 global production for seafood products. Mislabeling rates must be coupled with other data, like production, in order to understand potential ecological and economic consequences.



Over 350 products have been identified as substitutes worldwide. The majority (69%) have been identified from a single study, while almost half have been identified a total of two times or less (total number of samples). Striped Catfish (Pangasianodon hypophthalmus) has been identified by the most number of studies (n = 26), followed by Alaska Pollock (Gadus chalcogrammus, 19), Bigeye Tuna (Thunnus obesus, 14), Atlantic Cod (Gadus morhua, 13), Haddock (Melanogrammus aeglefinus, 13), and Atlantic Salmon (Salmo salar, 13).

We provide the substitutes that have been identified for particular seafood products, including from which country there were identified.

WANT TO KNOW MORE? The data, methods, and results presented here have been subject to peer-review and published in the scientific journal Biological Conservation. You can download the in-depth study here.