Table 1.
The terminology used in the analyses of seafood mislabeling and substitution, following Munguia-Vega et al. [2021].
Table 2.
Patterns of mislabeling for 316 samples sold under the 18 most common commercial names in Mexico, representing 84% of all the collected samples (See Table 1 for detailed descriptions of each term used in the column headers).
Fig 1.
Alluvial plot showing the patterns of 116 instances of mislabeling found in Mexico, displayed as a network connecting 53 species used as substitutes for 32 commercial names that created 90 unique combinations under which samples were mislabeled.
Line widths represent the frequency of a given mislabeling combination (thickest line = 12 events). The six commercial names with the highest frequency of mislabeling are shown with distinct colors.
Table 3.
Patterns of fish mislabeling found in three cities within Mexico, including sample size (N) and mislabeling rates (% M).
Fig 2.
Patterns of substitution of species within and between three distinct broad groups: a) wild marine bony fishes; b) wild marine elasmobranchs; c) freshwater and anadromous bony fishes from aquaculture. Arrows show each of eight observed types of substitutions, and numbers show the observed frequency (%) of each type. Line widths are proportional to the frequency of a given substitution combination observed. The possible substitution combinations are 1) substitution between two marine bony fishes; 2) substitution of an elasmobranch by a marine bony fish; 3) substitution of a marine bony fish by an elasmobranch; 4) substitution of a marine bony fish by a freshwater bony fish from aquaculture 5) substitution between freshwater bony fishes from aquaculture; 6) substitution of a bony fish from aquaculture by a marine bony fish; 7) substitution between elasmobranchs; 8) substitution of an elasmobranch by a freshwater bony fish from aquaculture. The substitution of a freshwater fish from aquaculture by an elasmobranch was the only possible substitution type not observed.
Fig 3.
Linear regression analyses showing: A) the relationship of net availability of a species in our dataset (Confirmed number of samples, excluding mislabeling and including samples used to substitute other species) as a predictor of mislabeling rates for the 18 most important commercial names found within three cities of Mexico; B) the relationship of the number of substitute species sold under the name of the focal species (Mislabeling diversity, a proxy of demand for commercial species in our dataset) as a predictor of mislabeling rates.
Fig 4.
Linear regression analysis shows the relationship between landing and import data for nine of the main commercial names found in Mexico as a predictor of observed mislabeling rates.
Table 4.
List of 20 threatened and near threatened species identified in this study, including scientific name, common name, commercial name under which they were sold, if the sample was mislabeled or not, IUCN red list category, and if the species is included in any of CITES appendices, and total number of samples identified.