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Are we sure we eat what we buy? Fish mislabelling in Buenos Aires province, the largest sea food market in Argentina
Fisheries Research 221 (2020) 105373
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Are we sure we eat what we buy? Fish mislabelling in Buenos Aires province, the largest sea food market in Argentina
T
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G. Delpiania,b, , S.M. Delpiania,b, M.Y. Deli Antonib, M. Covatti Aleb, L. Fischerb, L.O. Luciforac, J.M. Díaz de Astarloaa,b Instituto de Investigaciones Marinas y Costeras (IIMyC – CONICET), Rodríguez Peña 4046, Nivel 1, Mar del Plata, Argentina Laboratorio de Biotaxonomía Morfológica y Molecular de Peces (BIMOPE), Universidad Nacional de Mar del Plata, Funes 3350, Nivel -1, Mar del plata, Argentina c Instituto Nacional de Limnología, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ruta Nacional 168 km 0, Paraje El Pozo, Ciudad de Santa Fe, Santa Fe S3001XAI, Argentina a
b
A R T I C LE I N FO
A B S T R A C T
Handled by J Viñas
The detection of substituted or mislabeled seafood may have different consequences on consumers and the environment, including economic losses due to potential commercial fraud, public health effects, uncontrolled impacts on fish species of threatened status, and damage to populations due to overfishing. The molecular identification of processed meat or specimens that lack diagnostic body parts is a highly effective tool for species identification and law enforcement. DNA barcoding was used to assess levels of mislabeling or substitution through molecular identification of fresh fish fillets sold in different seafood markets throughout the Province of Buenos Aires, Argentina. The total substitution rate was 21.34% with most of the replacements involving Chondrichthyes (22 of the 35 mislabeled fillets), mainly the sharks Galeorhinus galeus (8 times) and Mustelus schmitii (9 times) being sold as something else. These results highlight the problems generated by mislabelling, to a great extent an economic problem (fraud), and also a conservation problem, due to the exploitation of threatened species. The present study reinforces a calling for enlarged traceability of food products and the assessment of authenticity of fillets by skilled supervisory authorities.
Keywords: DNA barcode Fraud COI Food traceability Conservation
1. Introduction Trading of fish products has significantly increased in recent years with the global total of marine catch of 79.3 million tonnes in 2016 (Food and Agriculture Organization (FAO, 2018). In Argentina during 2017, 775.792 t of fish products were produced. In the last years, the fishing industry has undergone several changes, such as diversification, innovations and improvement of its products, increasing production, marketing and worldwide distribution of fish products, and technological advances in production (Marko et al., 2004; Food and Agriculture Organization (FAO, 2016). Proper food labelling is important for legal, health and environmental issues. Recently, there has been an increasing interest by the consumers on detailed composition of the marketed food. Therefore, it is of great importance the presence of labels, together with other information commonly given, such as energy values and nutritional information (Barbuto et al., 2010). These changes make necessary the use of new tools to check the origin and authenticity of commercialized fish products. In addition, food safety concerns are pushing the need for
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accurately labeled food products, especially for fish products, such as fillets, that are indistinguishable on a morphological base. Fish species replacement or mislabeling – i.e. a situation in which the commercial name used does not correspond to the actual species used to manufacture the product – has multiple effects. The detection of substituted or mislabeled seafood, may have different consequences on consumers and the environment, including economic losses due to potential commercial fraud (Von der Heiden et al., 2010; Hanner et al., 2011; Carvalho et al., 2017), public health effects (Chang et al., 2008), uncontrolled impacts on threatened fish species (Ardura et al., 2011), and damage to populations due to overfishing (Tokeshi et al., 2013). The use of unclear labels makes it problematic for consumers to exercise their right to elude species of higher conservation concern or those associated with specific health issues. It is very important to consider the effect of these practices on threatened species, since fish retailers may offer for sale-endangered species, or those whose international trade is prohibited. An estimated 100 million sharks are killed every year, many of them for their fins, depleting shark stocks at an alarming rate (Vallianos
Corresponding author at: Instituto de Investigaciones Marinas y Costeras (IIMyC – CONICET), Rodríguez Peña 4046, Nivel 1, Mar del Plata, Argentina. E-mail address: [email protected] (G. Delpiani).
https://doi.org/10.1016/j.fishres.2019.105373 Received 27 May 2019; Received in revised form 7 August 2019; Accepted 6 September 2019 0165-7836/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Map indicating the cities in which samplings were made along the coast of Buenos Aires province, Argentina.
Fish products have shown high rates of replacement of the most commercially valued species by less valuable species, resulting in economic losses (de Brito et al., 2015). When a highly-prized species becomes uncommon after being overfished, fish retailers replace it with still-common species of lower commercial prize, resulting in a commercial fraud (Pepe et al., 2007). This strategy of replacing highervalue species with lower-value ones is not uncommon (Hsieh et al., 2007). Fraudulent commercialization of fishery products has been reported in countries from South (Veneza et al., 2018) and North America (Hanner et al., 2011), Europe (Mariani et al., 2015), Africa (Cawthorn et al., 2012) and Asia (Chang et al., 2016). Currently, several countries, such as Brazil and South Africa, and the European Union have a legal framework and governmental regulatory programs that request appropriate species traceability and labelling (Filonzi et al., 2010; Department of Health, South Africa (DoH, 2010). However, no regulations for seafood product species identification exist in Argentina. Moreover, no studies of the potential substitution of high-value fish species for those of lower value, which is common practice in several countries, have been conducted in Argentina. The main goal of this work is to estimate levels of mislabeling or substitution through molecular identification of fresh fish fillets sold in different seafood markets throughout the Province of Buenos Aires (heretofore, Buenos Aires). Almost 75% of the fishing fleet is located in ports of Buenos Aires (e.g. Mar del Plata, Puerto Quequén, Bahía Blanca, General Lavalle), which account for 70% of the total marine fish landings in Argentina. The present study is the first of its kind for Argentina, focused on the extent of the mismatch between the market names and the actual species for some of the most common commercial marine fish species marketed in Buenos Aires.
et al., 2018). According to the IUCN Red List of Threatened Species, over one-fifth of the world’s shark and ray species are regarded as threatened (Bräutigam et al., 2015). Given that elasmobranchs are considered the most threatened group of marine fishes in the world (Davidson and Dulvy, 2017), overfishing has a profound negative impact on sharks due to the biological characteristics of this group. In the Southwest Atlantic, chondrichthyans are subjected to commercial fishing as bycatch and are also targeted by commercial, artisanal and recreational fisheries throughout the distribution area (Chiaramonte, 1998; Bornatowski et al., 2014). Genetic techniques for species identification are an effective tool for verification of seafood, and should be more broadly applied to preserve the veracity of certification and discouraging fraud (Ogden, 2008). The lack of morphological features that are traditionally used to identify animal species is a common problem with food products, making authenticity tests impossible without alternative identification methods (Wong and Hanner, 2008). DNA barcoding is a powerful tool to rapidly determine the taxonomic group of a given organism (Hebert et al., 2003). It can be used to discriminate between closely related taxa (Stoeckle et al., 2004), is easily comparable across different studies (Cline, 2012), and more relevant, it can be used as a universal tool for food traceability. DNA sequencing of the cytochrome c oxidase I (COI) mitochondrial gene, on which DNA barcoding is based, is used to identify patterns in the mislabelling of the fishery products. COI as a marker is comparatively robust, allowing amplification of suitable DNA fragments from not only fresh, but also degraded, processed or cooked material (Christiansen et al., 2018). It is also highly effective: Yancy et al. (2008) discovered that sequencing the COI gene allowed them to identify 100% of the fishery products that they tested. The US Food and Drug Administration (FDA) has validated the generation of DNA barcodes for seafood product identification (Handy et al., 2011). 2
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Table 1 Summary of all the samples analyzed, with mislabeled cases highlighted in bold. MdP: Mar del Plata; VG: Villa Gesell; M: Miramar; MH: Monte Hermoso; N: Necochea; P: Pinamar; SC: San clemente del Tuyu; SB: San Bernardo; ST: Santa Teresita; BB: Bahía Blanca. Correct names
Sold as
Family
Scientific name
Common name
Common name
Scientific name
Callorhinchidae
Callorhinchus callorynchus Callorhinchus callorynchus Myliobatis goodei Atlantoraja castelnaui Squatina guggenheim Squatina guggenheim Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Genidens barbus Odontesthes argentinensis Odontesthes incisa Nemadactylus bergi Anchoa marinii Engraulis anchoita Merluccius hubbsi Merluccius hubbsi Mugil liza Mullus argentinae Genypterus blacodes Genypterus blacodes Paralichthys orbignyanus Percophis brasiliensis Pseudopercis semifasciata Urophycis brasiliensis Oncorhynchus gorbuscha Cynoscion guatucupa Cynoscion guatucupa Macrodon atricauda Menticirrhus americanus Micropogonias furnieri Micropogonias furnieri Pogonias courbina Pogonias courbina Pogonias courbina Pogonias courbina Thunnus albacares Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus
Pez gallo Pez gallo Chucho Raya Pollo de mar Pollo de mar Cazón Cazón Cazón Cazón Cazón Cazón Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Bagre Pejerrey Cornalito Castañeta Anchoa Anchoíta Merluza Merluza Lisa Trilla Abadejo Abadejo Lenguado Pez palo Salmón de mar Brótola Salmón rosado Pescadilla Pescadilla Pescadilla real Burriqueta Corvina rubia Corvina rubia Corvina negra Corvina negra Corvina negra Corvina negra Atún Mero Mero Mero Mero Mero
Pez gallo Atún Raya Abadejo (cheek) Pollo de mar Atún Cazón Atún Palo rosado Pescadilla Mero Palomito Gatuzo Atún Palo rosado Pez palo Abadejo (cheek) Palomito Cazón Bagre Pejerrey Cornalito Besugo Anchoa Anchoita Merluza Merluzón Lisa Trilla Abadejo Abadejo (cheek) Lenguado Pez palo Salmón de mar Brótola Salmón rosado Pescadilla Mero Pescadilla real Perita Corvina rubia Pargo Corvina negra Salmonada Mora Vacío de mar Atún Mero Brótola Lenguado Chernia Pescadilla
Callorhinchus callorynchus Thunnus albacares Rajidae Genypterus blacodes Squatina guggenheim Thunnus albacares Galeorhinus galeus Thunnus albacares Mustelus schmitti Cynoscion guatucupa Acanthistius patachonicus – Mustelus schmitti Thunnus albacares Mustelus schmitti Percophis brasiliensis Genypterus blacodes – Galeorhinus galeus Genidens barbus Odontesthes argentinensis Odontesthes incisa Pagrus pagrus Anchoa marinii Engraulis anchoita Merluccius hubbsi Merluccius australis Mugil liza Mullus argentinae Genypterus blacodes Genypterus blacodes Paralichthys orbignyanus Percophis brasiliensis Pseudopercis semifasciata Urophycis brasiliensis Oncorhynchus gorbuscha Cynoscion guatucupa Acanthistius patachonicus Macrodon atricauda – Micropogonias furnieri Umbrina canosai Pogonias courbina – – – Thunnus albacares Acanthistius patachonicus Urophycis brasiliensis Paralichthys orbignyanus Polyprion americanus Cynoscion guatucupa
Myliobatidae Rajidae Squatinidae Triakidae
Ariidae Atherinopsidae Cheilodactylidae Engraulidae Merluccidae Mugilidae Mullidae Ophidiidae Paralichthyidae Percophidae Pinguipedidae Phycidae Salmonidae Sciaenidae
Scombridae Serranidae
Catch location
N°
MdP, VG MdP SC MdP MdP, M, N, VG M C, P, ST, VG M, N MdP MdP MdP N MH, P, SB MdP, SB MdP M N N P, ST SC MdP, N, P, ST, VG N, SC MdP SC, ST MdP, ST MdP, M, N, P, SC, ST, VG N, P SC MdP, P MdP, M, P, ST, VG Mdp, N, P MdP, M, N, BB, SC, ST, VG MdP, M, N MdP, N, P, ST, VG VG MdP, N, P, SC, ST MH, N, SC M SC ST P, ST, VG MdP M, P, SC, ST M SC SC MdP, N, ST MdP, N, P, VG MdP P N M
3 1 1 2 7 1 5 3 1 1 1 2 3 2 1 1 1 2 2 3 6 3 1 2 2 22 2 6 2 8 4 12 5 7 1 6 3 1 1 1 3 1 7 1 1 1 4 4 1 1 1 1
2. Materials and methods
2.2. DNA extraction, amplification and sequencing
2.1. Sample collection
DNA extraction and amplification of a fragment of the mitochondrial cytochrome c oxidase subunit I gene (COI) were performed at the Argentine International Barcode of Life Reference Laboratory (IIMyC, CONICET, Mar del Plata, Argentina). DNA extraction and polymerase chain reaction (PCR) were performed in accordance with standard DNA barcoding protocols (Ivanova et al., 2006). Different sets of primer cocktails, including those designed for fishes (Ivanova et al., 2007), were used to amplify sequences. Each amplification reaction contained 2 μL DNA template, 6.25 μL 10% trehalose, 2 μL molecular biology grade water, 1.25 μL 10× reaction buffer, 0.625 μL MgCl2 (50 μM), 0.0625 μL dNTP (10 mM), 0.0625 μL of each primer (10 μM) and 0.0625 μL Invitrogen's Platinum Taq. Polymerase (5U μL−1) producing a total reaction volume of
We analyzed 172 fish products sold as fillets, consisting of 35 commercial fish names acquired from 24 fish retailers in 11 coastal cities of Buenos Aires (from north to south: San Clemente del Tuyú, Santa Teresita, San Bernardo, Pinamar, Villa Gesell, Mar del Plata, Miramar, Necochea, Claromecó, Monte Hermoso and Bahía Blanca), Argentina (Fig. 1). Samples were collected during August (austral winter) and February (austral summer). Muscle tissue samples were taken from the fillets and stored in ethanol 95% and conserved at −20 °C to be later processed at the molecular genetics laboratory.
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12.375 μL. The PCR profile comprised an initial step of 2 min at 95 °C, and 35 cycles of 30 s at 94 °C, 40 s at 52 °C and 1 min at 72 °C, with a final extension at 72 °C for 10 min. E-Gels (Invitrogen) were used to screen for amplification success. Sequencing of the COI gene was carried out at Macrogen, Inc. (Korea).
Umbrina canosai (once), G. galeus by M. schmitti (once). Taking into account all the samples, the highest replacement percentage occurred in the cities with the highest population and in those cities, the replacement reached up to 40% (Table 2). Most of the replacements involved Chondrichthyes (22 of the 35 mislabeled fillets), mainly the sharks G. galeus (8 times) and M. schmitii (9 times) being sold as something else.
2.3. Molecular data analysis
4. Discussion
Original and downloaded DNA sequences were combined into a single sequence initially analyzed using the program Geneious Pro 5.4.2 alignment (Drummond et al., 2018) to perform molecular analyses. Sequences were aligned using MUSCLE (Edgar, 2004), under default parameters and the alignment was inspected by eye for any obvious misalignments and edited using AliView (Lassmann et al., 2009). Nucleotide variation, substitution patterns, and genetic distances were examined using MEGA 7.0 (Kumar et al., 2016). The best-fit nucleotide evolution model for COI gene was evaluated under the informationtheoretic measure of Akaike Information Criterion with corrections for small sample sizes (AICc). Phylogenetic relationships among haplotypes were estimated by maximum likelihood (ML) analyses using MEGA 7.0 and 1000 bootstrap pseudoreplicates were conducted to estimate node support values for the resulting phylogeny. A Kimura 2 parameters (K2P, Kimura, 1980) distance matrix was built for all possible pairwise comparisons of aligned sequences. A neighbour-joining (NJ) tree of K2P distances was created to provide a graphic representation of divergences among species. The discontinuity between the mean intraspecific divergence and the smallest interspecific divergence between species (Barcode gap) was reported (Meier et al., 2008; Meyer and Paulay, 2005). The Barcode Index Number (BIN) was used to estimate the number of species directly from the barcode sequences (Ratnasingham and Hebert, 2013). The concordance of BIN estimates with the NJ and ML tree topologies was addressed. Sequences were compared with those published sequences that reached the standard to get a Barcode Index Number, using the specimen identification tool provided in the Barcode of Life Data System (BOLD) (Ratnasingham and Hebert, 2013).
This work is the first estimation of the extent of fish mislabelling in Argentina, based on direct sequencing of COI gene for species identification of fish fillets sold in the Province of Buenos Aires, the largest seafood market in Argentina. Overall, 35 of the 164 (21.34%) fillet samples analyzed from seafood retailers were genetically identified as different species to the commercial name used and indicated on the product’s label. Most often, the species used as a replacement is in fact cheaper or more easily available than the one in the label (BénardCapelle et al., 2015), and could consequently be considered fraudulent. The frequency of fish mislabelling observed here is comparable to results of similar studies in Brazil (Carvalho et al., 2017), North America (Logan et al., 2008; Marko et al., 2004; Wong and Hanner, 2008), South Africa (Cawthorn et al., 2012), and Europe (Barbuto et al., 2010; Filonzi et al., 2010; Miller and Mariani, 2010; Pappalardo and Ferrito, 2015), emphasizing that this problem is pervasive on a worldwide scale. DNA sequencing studies focused upon particular fisheries have also exposed consistently high levels of mislabelling. Rockfish (Logan et al., 2008), tuna from sushi restaurants (Lowenstein et al., 2009) and red snapper (Marko et al., 2004) in the USA, cod (Miller and Mariani, 2010) and hake (Machado-Schiaffino et al., 2008) in Europe, and various threatened local species in South Africa (von der Heyden et al., 2010), all had rates of market substitution of 25% or higher. Seafood mislabelling may occur accidentally, probably linked to inaccurate morphological identification of fishes, or may be deliberate. In this work, Myliobatis goodei sold as a skate species, or Mustelus schmitii sold as Galeorhinus galeus, can be regarded as accidental mislabelling, most probably due to the resemblance between these species pairs. Confusion may also arise as species may have dissimilar vernacular names in different regions, or share a vernacular name with another species (Di Pinto et al., 2015). Furthermore, invented names are sometimes applied to species that already have a widely known common name. In the present work, 10 of the 35 mislabeled samples, were marketed with invented names that do not correspond to the official common names. For instance, “pollo de mar” was used for Squatina guggenheim, which is widely known in Argentina as “pez ángel”; “Perita” was used for identifying Menticirrhus americanus in the market, instead of the vernacular name “Burriqueta”; “Salmonada”, “Mora” and “Vacío de mar” were used for Pogonias courbina; “Palomito” was Galeorhinus galeus and “Palo rosado” was used either for Mustelus schmitii and G. galeus (Table 1). The latter case causes confusion because Mustelus schmitti was sold in the market as “Palo rosado”, which is its corresponding tradename, but the official common name is “Gatuzo”. This problem also becomes visible in cases in which different species belonging to the same genus or family, are grouped under the same common name (Barbuto et al., 2010; Cawthorn et al., 2012), reducing the importance of specific identification, such as in flounders or silversides. Our results indicate that standardization of fish market names is needed in Argentina. Species with greater number of congenerics may be more susceptible to mislabelling (Carvalho et al., 2017). The generic common names may compromise the ability of consumers to make informed choices when buying seafood (Chang et al., 2016). Consequently standard market names for seafood products are needed in order to prevent confusion. In fact, confusion in fish nomenclature has been one of the driving factors for the compilation of lists of “acceptable market names” for seafood products by many countries, such as Brazil’s
3. Results We successfully obtained 164 sequences from the 172 fillet samples acquired, representing a 95.35% success rate. In total, 28 different species were detected belonging to 22 families. Six species belonged to the group of sharks, rays and chimaeras (class Chondrichthyes) and 22 were bony fishes (class Actinopterygii). Sciaenidae had the largest number of species (six), followed by Atherinopsidae and Triakidae (two each). All other families were represented by one species each (Table 1). The length of DNA barcode sequences ranged from 492 to 654 base pairs (bp), all of which were of high quality without insertions, deletions, or stop codons. The results of the comparison with the BOLD System database generated a similarity of between 98.87% and 99.84%. Phylogenetic analysis produced cleared and well supported separation at the species level, allowing for a definitive identification (Fig. 2). On the basis of these results, 28 clusters had a BIN assignment, all of them with species assignment, in 27 genera and 22 families. In total, 35 cases of mislabeling were revealed, representing a substitution rate of 21.34% of the total. The majority of mislabeling incidents (7) were found with Thunnus albacares, which was replaced by either Callorhinchus callorynchus (once), Squatina guggenheim (once), Galeorhinus galeus (3 times) or Mustelus schmitti (2 times). Three samples were sold as Genypterus blacodes but were actually either Atlantoraja castelnaui (2 times) or M. schmitti (once). Four samples of Acanthistius patachonicus were replaced by Polyprion americanus (once), by Urophycis brasiliensis (once), Paralichthys orbignyanus (once), and by Cynoscion guatucupa (once). Other replacements were: Merluccius australis replaced by M. hubbsi (twice), Pagrus pagrus replaced by Nemadactylus bergi (once), skates by Myliobatis goodei (once), Micropogonias furnieri by 4
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Fig. 2. Molecular phylogenetic tree obtained by the neighbor joining method. The common names written in bold are those that were detected as mislabeled. MEXXX code refers to the voucher reference. The number in branches refer to the number of sequences of the same species.
(Atlantoraja castelnaui) (US$3.80) and Mustelus schmitti (US$5.50), or G. galeus (US$4.50) sold as Acanthistius patachonicus (US$9.60), or Callorhinchus callorynchus (US$4.25) and M. schmitti sold as tuna (US $7.70), or A. patachonicus as Polyprion americanus (US$12.60) (prices correspond to 1 kg of fish fillet). Something similar happens in the United States with the “white tuna” sushi sample, typically considered a more valuable sushi made from “albacore tuna”, was instead “tilapia”, a much less expensive fish (Wong and Hanner, 2008). These authors also found that the seven mislabeled “red snappers” (the accepted vernacular name for Lutjanus campechanus) were identified as belonging to five different species, each from a different genus. In fact, 77% of the fish labelled as “red snapper” in the US have been found to be substituted with less expensive and/or overexploited species (Marko et al., 2004). In South Africa, it has long been recognized that the popular and highly-valued kingklip (Genypterus capensis) is prone to market substitution, whose stocks were heavily exploited in the 1980s by an experimental longline fishery in this country and these have not since recovered to their former abundance (McLean and Glazewski, 2009). In Argentina, the most commonly consumed marine fish is the common hake, Merluccius hubbsi, whose stocks are either overexploited or depleted (Food and Agriculture Organization (FAO, 2005), and in consequence, there is a necessity for using previously underutilized species. This situation, together with the growing price of seafood products, has led to fraud with the substitution using lower-price species. The increasing trend observed in shark meat trade in many trading countries suggest that underlying demand for these products is increasing (Dent and Clarke, 2015). Thus, there are likely to be areas where demand for shark meat is sufficiently high such that, even if demand for shark fins declines, existing fishing pressure will not. South Atlantic shark populations are under intense fishing pressure (Barreto et al., 2016), given that market demands have now shifted from fins to meat. On many occasions, shark meat may represent an underutilized resource, so it is possible that markets may be turning to sharks in order to provide protein supplies, as traditional fisheries reach levels of full utilization or overutilization (Dent and Clarke, 2015). This can be clearly seen in this work, since most of the fish substitutions were made with chondrichthyan species, mainly with sharks of the family Triakidae. Both M. schmitii and G. galeus were used to replace several species such as Thunnus albacares, Cynoscion guatucupa, Genypterus blacodes and Percophis brasiliensis. In addition, these sharks were those that
Table 2 Total percentage of mislabeling distributed by cities and within each city. Cities
% over total
% per city
Mar del Plata Miramar Necochea Pinamar San Clemente Santa Teresita San Bernardo
6.13 3.68 5.52 1.84 1.84 1.23 0.61
25.64 40.00 28.13 16.67 13.64 13.33 50.00F
Ministry of Agriculture, Livestock, and Food Supply (MAPA, 2016); Canada’s Food Inspection Agency Fish List (Hanner et al., 2011), the FDA’s seafood list in the USA, and those lists compiled by member states of the European Union (Bord Iascaigh Mhara (BIM, 2001; Canadian Food Inspection Agency (CFIA, 2018; Food and Drug Administration (FDA, 2010; Food Standards Agency (FSA, 2012), in order to facilitate market regulation. Even in the case that the aforementioned mislabelling incidents were unintentional, our results highlight the need for uniform fish naming in Argentina as a means of promoting fair trade, conservation efforts and consumer rights. In Argentina there is an official list, established by the Secretaría de Políticas, Regulación e Institutos and the Secretaría de Agregado de Valor (joint resolution No. 6-E/2018; Cámara de Importadores de la República Argentina (CIRA, 2018), however this list has not been disclosed, nor is it required or its use enforced. Nevertheless, the establishment of such lists in Argentina could provide some relief to the problems of mislabelling observed in this study, and although it is unlikely that these alone will completely eliminate the problem, it would be a very important first step. A number of cases of potentially deliberate mislabelling were uncovered, where financial incentives could have been a driving factor for the substitution of highly-valued fish species with lower-valued ones. Of greater concern, is that some traders may deliberately use mislabelling as a means to launder illegally-caught fish into legitimate markets, or simply to defraud consumers for the purpose of accruing greater profits (Ogden, 2008). In the present work, there is evidence of economic fraud as higher value species were replaced by another species of lesser value, as were the cases of replacing cheeks of Genypterus blacodes (US$12) with meat of the endangered spotback skate 5
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received the greatest number of unofficial vernacular names. This replacement could be due to the lack of availability of the above mentioned species, or the low sale of sharks under their actual name, or the combination of both. The most alarming cases of replacement are those involving species classified in the highest threat categories. These cases involve the sharks G. galeus, which is considered “Critically Endangered” by the IUCN (Walker et al., 2006) and whose population trend is decreasing (Barbini et al., 2015); M. schmitii, regarded as “Endangered” (Massa et al., 2006), and S. guggenheim, also categorized as “Endangered” (Oddone et al., 2019), as well as the “Endangered” skate A. castelnaui (Hozbor et al., 2004). Conservation measures for these species include several closed areas and marine protected areas (MPA), a Maximum Allowed Catch (MAC) established by the Argentine fisheries authorities on an annual basis, and a ban on landing sharks larger than 1.6 m in total length, for commercial fishing vessels (PAN – tiburones). However, there is still much room to improve the effectiveness of these measures. Closed areas and MPAs do not cover systematically the different chondrichthyan assemblages found off Argentina (Sabadin, 2019), and MACs and the ban on landing large sharks are deficiently enforced throughout the country. Consequently, approximately 47% of Argentina’s chondrichthyan fauna is considered to be in some extinction threat category (Vulnerable, Endangered or Critically Endangered) based on an international red list of endangered species (Massa et al., 2006). Our results indicate that mislabelling may be contributing to bypassing some of the conservation measures currently in place, namely MACs and the ban on landings of large sharks. More research is needed to explore substitution rates throughout Argentina. Our findings raise considerable concern on the functioning of the fisheries supply chain in Argentina and should compel authorities to identify targets for improving labelling policies, applicable to both domestic and imported products. For example, other commercial activities, such as restaurants, may have a greater incentive to mislabel than fish retail shops due to a greater price difference of their products. Modifications to the existing legislation should, at the very least, include the requirement for the declaration on product labels of a designated ‘acceptable market name’ and scientific name of the fish species being traded. An effort should be done to create a monitoring programme at national level, to lead an intense focus on seafood certification. Obviously, such regulations will be of little value if they are not properly enforced. The government will consequently also need to address questions on whether the current regulatory monitoring activities are adequate and in cases of fraudulent acts, it should be penalized according to the amount of mislabelling detected with substantial penalties, as it has being implemented in Brazil (Carvalho et al., 2017), and might greatly reduce the incidence of market substitution (Cline, 2012). If greater transparency can be achieved on the market, then public confidence might be restored in the seafood supply chain in Argentina and full efforts may be refocused on the conservation of the ocean's fish stocks.
2010. DNA barcoding reveals fraudulent substitutions in shark seafood products: the Italian case of “palombo” (Mustelus spp.). Food Res. Int. 43, 376–381. https://doi. org/10.1016/j.foodres.2009.10.009. Barreto, R.R., de Farias, W.K.T., Andrade, H., Santana, F.M., Lessa, R., 2016. Age, growth and spatial distribution of the life stages of the shortfin mako, Isurus oxyrinchus (Rafinesque, 1810) caught in the Western and Central Atlantic. PLoS One 11 (4), e0153062. https://doi.org/10.1371/journal.pone.0153062. Bénard-Capelle, J., Guillonneau, V., Nouvian, C., Fournier, N., Le Loët, K., Dettai, A., 2015. Fish mislabelling in France: substitution rates and retail types. PeerJ 2, e714. https://doi.org/10.7717/peerj.714. Bord Iascaigh Mhara (BIM), 2001. Information Note on the Labelling of Fishery and Aquaculture Products According to Council Regulation (EC) No 104/2000 and Commission Regulation (EC) No 2065/2001. BIM, Dublin, Ireland. Bornatowski, H., Braga, R.R., Abilhoa, V., Correa, M.F.M., 2014. Feeding ecology and trophic comparisons of six shark species in a coastal ecosystem of southern Brazil. J. Fish Biol. 85, 246–263. https://doi.org/10.1111/jfb.12417. Bräutigam, A., Callow, M., Campbell, I.R., Camhi, M.D., Cornish, A.S., Dulvy, N.K., Fordham, S.V., Fowler, S.L., Hood, A.R., McClennen, C., Reuter, E.L., Sant, G., Simpfendorfer, C.A., Welch, D.J., 2015. Global Priorities for Conserving Sharks and Rays: A 2015–2025 Strategy. Cámara de Importadores de la República Argentina (CIRA), 2018. Internet Document. Accessed 20/05/2019. http://www.cira.org.ar/index.php?option=com_content& view=article&id=8142:0702-18&catid=112&Itemid=500. Canadian Food Inspection Agency (CFIA), 2018. CFIA Fish List. Internet Document. Accessed 20/05/2019. www.inspection.gc.ca/english/fssa/fispoi/product/ comnome.shtml. Carvalho, D.C., Guedes, D., Trindade, M.G., Sartori Coelho, R.M., de Lima Araujo, P.H., 2017. Nationwide Brazilian governmental forensic programme reveals seafood mislabelling trends and rates using DNA barcoding. Fish. Res. 191, 30–35. https://doi. org/10.1016/j.fishres.2017.02.021. Cawthorn, D.M., Steinman, H.A., Witthuhn, R.C., 2012. DNA barcoding reveals a high incidence of fish species misrepresentation and substitution on the South African market. Food Res. Int. 46, 30–40. https://doi.org/10.1016/j.foodres.2011.11.011. Chang, S.-C., Kung, H.-F., Chen, H.-C., Lin, C.-S., Tsai, Y.-H., 2008. Determination of histamine and bacterial isolation in swordfish fillets (Xiphias gladius) implicated in a food borne poisoning. Food Control 19, 16–21. https://doi.org/10.1016/j.foodcont. 2007.01.005. Chang, C.H., Lin, H.Y., Ren, Q., Lin, Y.S., Shao, K.T., 2016. DNA barcode identification of fish products in Taiwan: government-commissioned authentication cases. Food Control 66, 38–43. https://doi.org/10.1016/j.foodcont.2016.01.034. Chiaramonte, G., 1998. Shark fisheries in Argentina. Mar. Freshw. Res. 49 (7), 601–609. https://doi.org/10.1071/MF97136. Christiansen, H., Fournier, N., Hellemans, B., Volckaert, F.A.M., 2018. Seafood substitution and mislabeling in Brussels’ restaurants and canteens. Food Control 85, 66–75. https://doi.org/10.1016/j.foodcont.2017.09.005. Cline, E., 2012. Marketplace substitution of Atlantic salmon for Pacific salmon in Washington State detected by DNA barcoding. Food Res. Int. 45, 388–393. https:// doi.org/10.1016/j.foodres.2011.10.043. Davidson, L.N., Dulvy, N.K., 2017. Global marine protected areas to prevent extinctions. Nat. Ecol. Evol. 1https://doi.org/10.1038/s41559-016-0040. 0040. Dent, F., Clarke, S., 2015. State of the Global Market for Shark Products. FAO Fisheries and Aquaculture Technical Paper No. 590. Rome, FAO. 187 pp.. . Department of Health, South Africa (DoH), 2010. Regulations Relating to the Labelling and Advertising of Foodstuffs. Foodstuffs, Cosmetics and Disinfectants Act, 1927. Act N° 54 of 1972, GNR 146/2010. Government Printing Works, Pretoria, South Africa, pp. 1–109. de Brito, M.A., Schneider, H., Sampaio, I., Santos, S., 2015. DNA barcoding reveals highsubstitution rate and mislabeling in croaker fillets (Sciaenidae) marketed in Brazil: the case of pescada branca (Cynoscion leiarchus and Plagioscion squamosissimus). Food Res. Int. 70, 40–46. https://doi.org/10.1016/j.foodres.2015.01.031. Di Pinto, A., Marchetti, P., Mottola, A., Bozzo, G., Bonerba, E., Ceci, E., Bottaro, M., Tantillo, G., 2015. Species identification in fish fillet products using DNA barcoding. Fish. Res. 170, 9–13. https://doi.org/10.1016/j.fishres.2015.05.006. Drummond, A.J., Ashton, B., Buxton, S., Cheung, M., Cooper, A., Duran, C., Field, M., Heled, J., Kearse, M., Markowitz, S., et al., 2018. Geneious. 5.1. Internet Document. Accessed 20/05/19. http://www.geneious.com. Edgar, R.C., 2004. Muscle: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf 5, 1–19. Filonzi, L., Chiesa, S., Vaghi, M., Marzano, F.N., 2010. Molecular barcoding reveals mislabelling of commercial fish products in Italy. Food Res. Int. 43, 1383–1388. https://doi.org/10.1016/j.foodres.2010.04.016. Food and Agriculture Organization (FAO), 2005. FAO’s Report “Review of the State of World Marine Fisheries Resources. FAO Fisheries Technical Paper. No. 457. FAO, Rome 235p. Food and Agriculture Organization (FAO), 2016. The State of World Fisheries and Aquaculture. Contributing to Food Security and Nutrition for All. Rome, FAO 200p. ISBN 978-92-5-109185-2. Food and Agriculture Organization (FAO), 2018. The State of World Fisheries and Aquaculture 2018 – Meeting the Sustainable Development Goals. FAO, Rome 227p. License: CC BY-NC-SA 3.0 IGO. Food and Drug Administration (FDA), 2010. The Seafood List — FDA’s Guide to Acceptable Market Names for Seafood Sold in Interstate Commerce. Internet Document. Accessed 20/05/2019. https://www.fda.gov/regulatory-information/ search-fda-guidance-documents/cpg-sec-540750-common-or-usual-names-seafoodinterstate-commerce. Food Standards Agency (FSA), 2012. Table of Commercial Designations for Specific Fish
Acknowledgements We would like to thank to D. Castellini, C. Spath and G. Blasina who collaborated in the samplings; and the anonymous referees for the suggestions. Funding was provided by Universidad Nacional de Mar del Plata (EXA 867/18), and CONICET by means of the iBOL Reference laboratory. References Ardura, A., Planes, S., Garcia-Vazquez, E., 2011. Beyond biodiversity: fish metagenomes. PLoS One 6 (8), e22592. https://doi.org/10.1371/journal.pone.0022592. Barbini, S.A., Lucifora, L.O., Figueroa, D.E., 2015. Using opportunistic records from a recreational fishing magazine to assess population trends of sharks. Can. J. Fish. Aquat. Sci. 72 (12), 1853–1859. https://doi.org/10.1139/cjfas-2015-0087. Barbuto, M., Galimberti, A., Ferri, E., Labra, M., Malandra, R., Galli, P., Casiraghi, M.,
6
Fisheries Research 221 (2020) 105373
G. Delpiani, et al.
McLean, B., Glazewski, J.I., 2009. Marine environments. In: Strydom, H.A., King, N.D. (Eds.), Fuggle & Rabie’s Environmental Management in South Africa, 2nd ed. Juta, South Africa, pp. 455–511. Meier, R., Zhang, G., Ali, F., 2008. The use of mean instead of smallest interspecific distances exaggerates the size of the “Barcoding Gap” and leads to misidentification. Syst. Biol. 57 (5), 809–813. https://doi.org/10.1080/10635150802406343. Meyer, C.P., Paulay, G., 2005. DNA Barcoding: error rates based on comprehensive sampling. PLoS Biol. 3 (12), e422. https://doi.org/10.1371/journal.pbio.0030422. Miller, D.D., Mariani, S., 2010. Smoke, mirrors, and mislabeled cod: poor transparency in the European seafood industry. Front. Ecol. Environ. 8, 517–521. https://doi.org/10. 1890/090212. Oddone, M., Awruch, C.A., Barreto, R., Charvet, P., Chiaramonte, G.E., Cuevas, J.M., Dolphine, P., Faria, V., Paesch, L., Rincon, G., Vooren, C.M., 2019. Squatina guggenheim. The IUCN Red List of Threatened Species 2019. e.T130393378A130393975. https://doi.org/10.2305/IUCN.UK.2019-1.RLTS.T130393378A130393975.en. Ogden, R., 2008. Fisheries forensics: the use of DNA tools for improving compliance, traceability and enforcement in the fishing industry. Fish Fish. 9, 462–472. https:// doi.org/10.1111/j.1467-2979.2008.00305.x. Pappalardo, A.M., Ferrito, V., 2015. DNA barcoding species identification unveils mislabeling of processed flatfish products in southern Italy markets. Fish. Res. 164, 153–158. https://doi.org/10.1016/j.fishres.2014.11.004. Pepe, T., Trotta, M., Di Marco, I., Anastasio, A., Bautista, J.M., Cortesi, M.L., 2007. Fish species identification in surimi-based products. J. Agric. Food Chem. 55, 3681–3685. https://doi.org/10.1021/jf063321o. Ratnasingham, S., Hebert, P.D.N., 2013. A DNA-based registry for all animal species: the Barcode Index Number (BIN) system. PLoS One 8 (7), e66213. https://doi.org/10. 1371/journal.pone.0066213. Sabadin, D.E., 2019. Patrones de distribución geográfica de la biodiversidad de tiburones, rayas y quimeras (Chondrichthyes) del Atlántico Sudoccidental. PhD Thesis. Universidad Nacional de Mar del Plata, Mar del Plata. Stoeckle, M., Waggoner, P., Ausubel, J., 2004. Barcoding Life: Ten Reasons. Consortium for the Barcode of Life (CBOL). Internet Document. Accessed 20/05/2019. www. barcoding.si.edu. Tokeshi, M., Arakaki, S., Daud, J.R.P., 2013. Consuming diversity: analysis of seasonal catch patterns in multispecies artisanal reef fisheries in North Sulawesi, Eastern Indonesia. Pac. Sci. 67 (1), 1–13. https://doi.org/10.2984/67.1.1. Vallianos, C., Sherry, J., Hofford, A., Baker, J., 2018. Sharks in Crisis: Evidence of Positive Behavioral Change in China as New Threats Emerge. Wildaid. Internet document, https://wildaid.org/wp-content/uploads/2018/02/WildAid-Sharks-in-Crisis-2018. pdf. Accessed 20/05/2019. Veneza, I., Da Silva, R., Freitas, L., Silva, S., Martins, K., Sampaio, I., Schneider, H., Gomes, G., 2018. Molecular authentication of Pargo fillets Lutjanus purpureus (Perciformes: Lutjanidae) by DNA barcoding reveals commercial fraud. Neotrop. Ichthyol. 16, e170068. https://doi.org/10.1590/1982-0224-20170068. von der Heyden, S., Barendse, J., Seebregts, A.J., Matthee, C.A., 2010. Misleading the masses: detection of mislabelled and substituted frozen fish products in South Africa. ICES J. Mar. Sci. 67, 176–185. https://doi.org/10.1093/icesjms/fsp222. Walker, T.I., Cavanagh, R.D., Stevens, J.D., Carlisle, A.B., Chiaramonte, G.E., Domingo, A., Ebert, D.A., Mancusi, C.M., Massa, A., McCord, M., Morey, G., Paul, L.J., Serena, F., Vooren, C.M., 2006. Galeorhinus galeus. The IUCN Red List of Threatened Species 2006. Internet document, https://doi.org/10.2305/IUCN.UK.2006.RLTS. T39352A10212764.en. e.T39352A10212764 Accessed 20/05/2019. . Wong, E.H.K., Hanner, R.H., 2008. DNA barcoding detects market substitution in North American seafood. Food Res. Int. 41, 828–837. https://doi.org/10.1016/j.foodres. 2008.07.005. Yancy, H.F., Zemlak, T.S., Mason, J.A., Washington, J., Tenge, B., Nguyen, N.–L., Barnett, J.D., Savary, W.E., Hill, W.E., Moore, M.M., Fry, F.S., Randolph, S.C., Rogers, P.L., Hebert, P.D., 2008. Potential use of DNA barcodes in regulatory science: applications of the Regulatory Fish Enciclopedia. J. Food Protect. 71, 210–217. https://doi.org/ 10.4315/0362-028X-71.1.210.
Species Derived From the Fish Labelling (England) Regulations 2010. Internet Document. Accessed 20/05/2019. https://assets.publishing.service.gov.uk/ government/uploads/system/uploads/attachment_data/file/82592/20120918-fishlabelling-consult-reg-guide-note.pdf. Handy, S.M., Deeds, J.R., Ivanova, N.V., Hebert, P.D., Hanner, R.H., Ormos, A., Weight, L., Moore, M.M., Yancy, H.F., 2011. A single laboratory validated method for the generation of DNA barcodes for the identification of fish for regulatory compliance. J. AOAC Int. 94, 201–210 https://doi.org/101093/bib/bbq004. Hanner, R., Becker, S., Ivanova, N.V., Steinke, D., 2011. FISH-BOL and seafood identification: geographically dispersed case studies reveal systemic market substitution across Canada. Mitochondrial DNA 22 (Suppl.1), 106–122. https://doi.org/10.3109/ 19401736.2011.588217. Hebert, P.D.N., Cywinska, A., Ball, S.L., deWaard, J.R., 2003. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10. 1098/rspb.2002.2218. Hozbor, N., Massa, A., Vooren, C.M., 2004. Atlantoraja castelnaui. The IUCN Red List of Threatened Species 2004. e.T44575A10921544. https://doi.org/10.2305/IUCN.UK. 2004.RLTS.T44575A10921544.en. Hsieh, H.-s., Chai, T.-j., Hwang, D.F., 2007. Using PCR-RFLP method to identify the species of different processed products of billfish meats. Food Control 18, 369–374. https://doi.org/10.1016/j.foodcont.2005.11.002. Ivanova, N.V., Dewaard, J., Hanner, P.D.N., 2006. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Mol. Ecol. Notes 6, 998–1002. https://doi. org/10.1111/j.1471-8286.2006.01428.x. Ivanova, N.V., Zemlak, T.S., Hanner, R.H., Hebert, P.D., 2007. Universal primer cocktails for fish DNA barcoding. Mol. Ecol. Notes 7, 544–548. https://doi.org/10.1111/j. 1471-8286.2007.01748.x. Kimura, M., 1980. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120. https://doi.org/10.1007/BF01731581. Kumar, S., Stecher, G., Tamura, K., 2016. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33 (7), 1870–1874. https:// doi.org/10.1093/molbev/msw054. Lassmann, T., Frings, O., Sonnhammer, E.L., 2009. Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features. Nucleic Acids Res. 37 (3), 858–865. https://doi.org/10.1093/nar/gkn1006. Logan, C.A., Alter, S.E., Haupt, A.J., Tomalty, K., Palumbi, S.R., 2008. An impediment to consumer choice: overfished species are sold as Pacific red snapper. Biol. Conserv. 141, 1591–1599. https://doi.org/10.1016/j.biocon.2008.04.007. Lowenstein, J.H., Amato, G., Kolokotronis, S.-O., 2009. The real maccoyii: identifying tuna sushi with DNA barcodes - contrasting characteristic attributes and genetic distances. PLoS One 4, e7866. https://doi.org/10.1371/journal.pone.0007866. Machado-Schiaffino, G., Martinez, J.L., Garcia-Vazquez, E., 2008. Detection of mislabelling in hake seafood employing mtSNPs-based methodology with identification of eleven hake species of the genus Merluccius. J. Agric. Food Chem. 56, 5091–5095. https://doi.org/10.1021/jf800207t. MAPA – Ministry of Agriculture, Livestock and Food Supply, 2016. Instrucão Normativa n°29, de 23 de setembro de 2015. Mariani, S., Griffiths, A.M., Velasco, A., Kappel, K., Jérôme, M., Perez-Martin, R.I., Schröder, U., Verrez-Bagnis, V., Silva, H., Vandamme, S.G., Boufana, B., Mendes, R., Shorten, M., Smith, C., Hankard, E., Hook, S.A., Weymer, A.S., Gunning, D., Sotelo, C.G., 2015. Low mislabeling rates indicate marked improvements in European seafood market operations. Front. Ecol. Environ. 13 (10), 536–540. https://doi.org/10. 1890/150119. Marko, P.B., Lee, S.C., Rice, A.M., Gramling, J.M., Fitzhenry, T.M., McAlister, J.S., Harper, G.R., Moran, A.L., 2004. Mislabelling of a depleted reef fish. Nature 430, 309–310. https://doi.org/10.1038/430309b10.1038/430309b. Massa, A., Hozbor, N., Chiaramonte, G.E., Balestra, A.D., Vooren, C.M., 2006. Mustelus schmitti. The IUCN Red List of Threatened Species 2006. https://doi.org/10.2305/ IUCN.UK.2006.RLTS.T60203A12318268.en. Internet document, e. T60203A12318268 Accessed 20/05/2019.
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Are we sure we eat what we buy? Fish mislabelling in Buenos Aires province, the largest sea food market in Argentina
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G. Delpiania,b, , S.M. Delpiania,b, M.Y. Deli Antonib, M. Covatti Aleb, L. Fischerb, L.O. Luciforac, J.M. Díaz de Astarloaa,b Instituto de Investigaciones Marinas y Costeras (IIMyC – CONICET), Rodríguez Peña 4046, Nivel 1, Mar del Plata, Argentina Laboratorio de Biotaxonomía Morfológica y Molecular de Peces (BIMOPE), Universidad Nacional de Mar del Plata, Funes 3350, Nivel -1, Mar del plata, Argentina c Instituto Nacional de Limnología, Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ruta Nacional 168 km 0, Paraje El Pozo, Ciudad de Santa Fe, Santa Fe S3001XAI, Argentina a
b
A R T I C LE I N FO
A B S T R A C T
Handled by J Viñas
The detection of substituted or mislabeled seafood may have different consequences on consumers and the environment, including economic losses due to potential commercial fraud, public health effects, uncontrolled impacts on fish species of threatened status, and damage to populations due to overfishing. The molecular identification of processed meat or specimens that lack diagnostic body parts is a highly effective tool for species identification and law enforcement. DNA barcoding was used to assess levels of mislabeling or substitution through molecular identification of fresh fish fillets sold in different seafood markets throughout the Province of Buenos Aires, Argentina. The total substitution rate was 21.34% with most of the replacements involving Chondrichthyes (22 of the 35 mislabeled fillets), mainly the sharks Galeorhinus galeus (8 times) and Mustelus schmitii (9 times) being sold as something else. These results highlight the problems generated by mislabelling, to a great extent an economic problem (fraud), and also a conservation problem, due to the exploitation of threatened species. The present study reinforces a calling for enlarged traceability of food products and the assessment of authenticity of fillets by skilled supervisory authorities.
Keywords: DNA barcode Fraud COI Food traceability Conservation
1. Introduction Trading of fish products has significantly increased in recent years with the global total of marine catch of 79.3 million tonnes in 2016 (Food and Agriculture Organization (FAO, 2018). In Argentina during 2017, 775.792 t of fish products were produced. In the last years, the fishing industry has undergone several changes, such as diversification, innovations and improvement of its products, increasing production, marketing and worldwide distribution of fish products, and technological advances in production (Marko et al., 2004; Food and Agriculture Organization (FAO, 2016). Proper food labelling is important for legal, health and environmental issues. Recently, there has been an increasing interest by the consumers on detailed composition of the marketed food. Therefore, it is of great importance the presence of labels, together with other information commonly given, such as energy values and nutritional information (Barbuto et al., 2010). These changes make necessary the use of new tools to check the origin and authenticity of commercialized fish products. In addition, food safety concerns are pushing the need for
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accurately labeled food products, especially for fish products, such as fillets, that are indistinguishable on a morphological base. Fish species replacement or mislabeling – i.e. a situation in which the commercial name used does not correspond to the actual species used to manufacture the product – has multiple effects. The detection of substituted or mislabeled seafood, may have different consequences on consumers and the environment, including economic losses due to potential commercial fraud (Von der Heiden et al., 2010; Hanner et al., 2011; Carvalho et al., 2017), public health effects (Chang et al., 2008), uncontrolled impacts on threatened fish species (Ardura et al., 2011), and damage to populations due to overfishing (Tokeshi et al., 2013). The use of unclear labels makes it problematic for consumers to exercise their right to elude species of higher conservation concern or those associated with specific health issues. It is very important to consider the effect of these practices on threatened species, since fish retailers may offer for sale-endangered species, or those whose international trade is prohibited. An estimated 100 million sharks are killed every year, many of them for their fins, depleting shark stocks at an alarming rate (Vallianos
Corresponding author at: Instituto de Investigaciones Marinas y Costeras (IIMyC – CONICET), Rodríguez Peña 4046, Nivel 1, Mar del Plata, Argentina. E-mail address: [email protected] (G. Delpiani).
https://doi.org/10.1016/j.fishres.2019.105373 Received 27 May 2019; Received in revised form 7 August 2019; Accepted 6 September 2019 0165-7836/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Map indicating the cities in which samplings were made along the coast of Buenos Aires province, Argentina.
Fish products have shown high rates of replacement of the most commercially valued species by less valuable species, resulting in economic losses (de Brito et al., 2015). When a highly-prized species becomes uncommon after being overfished, fish retailers replace it with still-common species of lower commercial prize, resulting in a commercial fraud (Pepe et al., 2007). This strategy of replacing highervalue species with lower-value ones is not uncommon (Hsieh et al., 2007). Fraudulent commercialization of fishery products has been reported in countries from South (Veneza et al., 2018) and North America (Hanner et al., 2011), Europe (Mariani et al., 2015), Africa (Cawthorn et al., 2012) and Asia (Chang et al., 2016). Currently, several countries, such as Brazil and South Africa, and the European Union have a legal framework and governmental regulatory programs that request appropriate species traceability and labelling (Filonzi et al., 2010; Department of Health, South Africa (DoH, 2010). However, no regulations for seafood product species identification exist in Argentina. Moreover, no studies of the potential substitution of high-value fish species for those of lower value, which is common practice in several countries, have been conducted in Argentina. The main goal of this work is to estimate levels of mislabeling or substitution through molecular identification of fresh fish fillets sold in different seafood markets throughout the Province of Buenos Aires (heretofore, Buenos Aires). Almost 75% of the fishing fleet is located in ports of Buenos Aires (e.g. Mar del Plata, Puerto Quequén, Bahía Blanca, General Lavalle), which account for 70% of the total marine fish landings in Argentina. The present study is the first of its kind for Argentina, focused on the extent of the mismatch between the market names and the actual species for some of the most common commercial marine fish species marketed in Buenos Aires.
et al., 2018). According to the IUCN Red List of Threatened Species, over one-fifth of the world’s shark and ray species are regarded as threatened (Bräutigam et al., 2015). Given that elasmobranchs are considered the most threatened group of marine fishes in the world (Davidson and Dulvy, 2017), overfishing has a profound negative impact on sharks due to the biological characteristics of this group. In the Southwest Atlantic, chondrichthyans are subjected to commercial fishing as bycatch and are also targeted by commercial, artisanal and recreational fisheries throughout the distribution area (Chiaramonte, 1998; Bornatowski et al., 2014). Genetic techniques for species identification are an effective tool for verification of seafood, and should be more broadly applied to preserve the veracity of certification and discouraging fraud (Ogden, 2008). The lack of morphological features that are traditionally used to identify animal species is a common problem with food products, making authenticity tests impossible without alternative identification methods (Wong and Hanner, 2008). DNA barcoding is a powerful tool to rapidly determine the taxonomic group of a given organism (Hebert et al., 2003). It can be used to discriminate between closely related taxa (Stoeckle et al., 2004), is easily comparable across different studies (Cline, 2012), and more relevant, it can be used as a universal tool for food traceability. DNA sequencing of the cytochrome c oxidase I (COI) mitochondrial gene, on which DNA barcoding is based, is used to identify patterns in the mislabelling of the fishery products. COI as a marker is comparatively robust, allowing amplification of suitable DNA fragments from not only fresh, but also degraded, processed or cooked material (Christiansen et al., 2018). It is also highly effective: Yancy et al. (2008) discovered that sequencing the COI gene allowed them to identify 100% of the fishery products that they tested. The US Food and Drug Administration (FDA) has validated the generation of DNA barcodes for seafood product identification (Handy et al., 2011). 2
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Table 1 Summary of all the samples analyzed, with mislabeled cases highlighted in bold. MdP: Mar del Plata; VG: Villa Gesell; M: Miramar; MH: Monte Hermoso; N: Necochea; P: Pinamar; SC: San clemente del Tuyu; SB: San Bernardo; ST: Santa Teresita; BB: Bahía Blanca. Correct names
Sold as
Family
Scientific name
Common name
Common name
Scientific name
Callorhinchidae
Callorhinchus callorynchus Callorhinchus callorynchus Myliobatis goodei Atlantoraja castelnaui Squatina guggenheim Squatina guggenheim Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Galeorhinus galeus Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Mustelus schmitti Genidens barbus Odontesthes argentinensis Odontesthes incisa Nemadactylus bergi Anchoa marinii Engraulis anchoita Merluccius hubbsi Merluccius hubbsi Mugil liza Mullus argentinae Genypterus blacodes Genypterus blacodes Paralichthys orbignyanus Percophis brasiliensis Pseudopercis semifasciata Urophycis brasiliensis Oncorhynchus gorbuscha Cynoscion guatucupa Cynoscion guatucupa Macrodon atricauda Menticirrhus americanus Micropogonias furnieri Micropogonias furnieri Pogonias courbina Pogonias courbina Pogonias courbina Pogonias courbina Thunnus albacares Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus Acanthistius patachonicus
Pez gallo Pez gallo Chucho Raya Pollo de mar Pollo de mar Cazón Cazón Cazón Cazón Cazón Cazón Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Gatuzo Bagre Pejerrey Cornalito Castañeta Anchoa Anchoíta Merluza Merluza Lisa Trilla Abadejo Abadejo Lenguado Pez palo Salmón de mar Brótola Salmón rosado Pescadilla Pescadilla Pescadilla real Burriqueta Corvina rubia Corvina rubia Corvina negra Corvina negra Corvina negra Corvina negra Atún Mero Mero Mero Mero Mero
Pez gallo Atún Raya Abadejo (cheek) Pollo de mar Atún Cazón Atún Palo rosado Pescadilla Mero Palomito Gatuzo Atún Palo rosado Pez palo Abadejo (cheek) Palomito Cazón Bagre Pejerrey Cornalito Besugo Anchoa Anchoita Merluza Merluzón Lisa Trilla Abadejo Abadejo (cheek) Lenguado Pez palo Salmón de mar Brótola Salmón rosado Pescadilla Mero Pescadilla real Perita Corvina rubia Pargo Corvina negra Salmonada Mora Vacío de mar Atún Mero Brótola Lenguado Chernia Pescadilla
Callorhinchus callorynchus Thunnus albacares Rajidae Genypterus blacodes Squatina guggenheim Thunnus albacares Galeorhinus galeus Thunnus albacares Mustelus schmitti Cynoscion guatucupa Acanthistius patachonicus – Mustelus schmitti Thunnus albacares Mustelus schmitti Percophis brasiliensis Genypterus blacodes – Galeorhinus galeus Genidens barbus Odontesthes argentinensis Odontesthes incisa Pagrus pagrus Anchoa marinii Engraulis anchoita Merluccius hubbsi Merluccius australis Mugil liza Mullus argentinae Genypterus blacodes Genypterus blacodes Paralichthys orbignyanus Percophis brasiliensis Pseudopercis semifasciata Urophycis brasiliensis Oncorhynchus gorbuscha Cynoscion guatucupa Acanthistius patachonicus Macrodon atricauda – Micropogonias furnieri Umbrina canosai Pogonias courbina – – – Thunnus albacares Acanthistius patachonicus Urophycis brasiliensis Paralichthys orbignyanus Polyprion americanus Cynoscion guatucupa
Myliobatidae Rajidae Squatinidae Triakidae
Ariidae Atherinopsidae Cheilodactylidae Engraulidae Merluccidae Mugilidae Mullidae Ophidiidae Paralichthyidae Percophidae Pinguipedidae Phycidae Salmonidae Sciaenidae
Scombridae Serranidae
Catch location
N°
MdP, VG MdP SC MdP MdP, M, N, VG M C, P, ST, VG M, N MdP MdP MdP N MH, P, SB MdP, SB MdP M N N P, ST SC MdP, N, P, ST, VG N, SC MdP SC, ST MdP, ST MdP, M, N, P, SC, ST, VG N, P SC MdP, P MdP, M, P, ST, VG Mdp, N, P MdP, M, N, BB, SC, ST, VG MdP, M, N MdP, N, P, ST, VG VG MdP, N, P, SC, ST MH, N, SC M SC ST P, ST, VG MdP M, P, SC, ST M SC SC MdP, N, ST MdP, N, P, VG MdP P N M
3 1 1 2 7 1 5 3 1 1 1 2 3 2 1 1 1 2 2 3 6 3 1 2 2 22 2 6 2 8 4 12 5 7 1 6 3 1 1 1 3 1 7 1 1 1 4 4 1 1 1 1
2. Materials and methods
2.2. DNA extraction, amplification and sequencing
2.1. Sample collection
DNA extraction and amplification of a fragment of the mitochondrial cytochrome c oxidase subunit I gene (COI) were performed at the Argentine International Barcode of Life Reference Laboratory (IIMyC, CONICET, Mar del Plata, Argentina). DNA extraction and polymerase chain reaction (PCR) were performed in accordance with standard DNA barcoding protocols (Ivanova et al., 2006). Different sets of primer cocktails, including those designed for fishes (Ivanova et al., 2007), were used to amplify sequences. Each amplification reaction contained 2 μL DNA template, 6.25 μL 10% trehalose, 2 μL molecular biology grade water, 1.25 μL 10× reaction buffer, 0.625 μL MgCl2 (50 μM), 0.0625 μL dNTP (10 mM), 0.0625 μL of each primer (10 μM) and 0.0625 μL Invitrogen's Platinum Taq. Polymerase (5U μL−1) producing a total reaction volume of
We analyzed 172 fish products sold as fillets, consisting of 35 commercial fish names acquired from 24 fish retailers in 11 coastal cities of Buenos Aires (from north to south: San Clemente del Tuyú, Santa Teresita, San Bernardo, Pinamar, Villa Gesell, Mar del Plata, Miramar, Necochea, Claromecó, Monte Hermoso and Bahía Blanca), Argentina (Fig. 1). Samples were collected during August (austral winter) and February (austral summer). Muscle tissue samples were taken from the fillets and stored in ethanol 95% and conserved at −20 °C to be later processed at the molecular genetics laboratory.
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12.375 μL. The PCR profile comprised an initial step of 2 min at 95 °C, and 35 cycles of 30 s at 94 °C, 40 s at 52 °C and 1 min at 72 °C, with a final extension at 72 °C for 10 min. E-Gels (Invitrogen) were used to screen for amplification success. Sequencing of the COI gene was carried out at Macrogen, Inc. (Korea).
Umbrina canosai (once), G. galeus by M. schmitti (once). Taking into account all the samples, the highest replacement percentage occurred in the cities with the highest population and in those cities, the replacement reached up to 40% (Table 2). Most of the replacements involved Chondrichthyes (22 of the 35 mislabeled fillets), mainly the sharks G. galeus (8 times) and M. schmitii (9 times) being sold as something else.
2.3. Molecular data analysis
4. Discussion
Original and downloaded DNA sequences were combined into a single sequence initially analyzed using the program Geneious Pro 5.4.2 alignment (Drummond et al., 2018) to perform molecular analyses. Sequences were aligned using MUSCLE (Edgar, 2004), under default parameters and the alignment was inspected by eye for any obvious misalignments and edited using AliView (Lassmann et al., 2009). Nucleotide variation, substitution patterns, and genetic distances were examined using MEGA 7.0 (Kumar et al., 2016). The best-fit nucleotide evolution model for COI gene was evaluated under the informationtheoretic measure of Akaike Information Criterion with corrections for small sample sizes (AICc). Phylogenetic relationships among haplotypes were estimated by maximum likelihood (ML) analyses using MEGA 7.0 and 1000 bootstrap pseudoreplicates were conducted to estimate node support values for the resulting phylogeny. A Kimura 2 parameters (K2P, Kimura, 1980) distance matrix was built for all possible pairwise comparisons of aligned sequences. A neighbour-joining (NJ) tree of K2P distances was created to provide a graphic representation of divergences among species. The discontinuity between the mean intraspecific divergence and the smallest interspecific divergence between species (Barcode gap) was reported (Meier et al., 2008; Meyer and Paulay, 2005). The Barcode Index Number (BIN) was used to estimate the number of species directly from the barcode sequences (Ratnasingham and Hebert, 2013). The concordance of BIN estimates with the NJ and ML tree topologies was addressed. Sequences were compared with those published sequences that reached the standard to get a Barcode Index Number, using the specimen identification tool provided in the Barcode of Life Data System (BOLD) (Ratnasingham and Hebert, 2013).
This work is the first estimation of the extent of fish mislabelling in Argentina, based on direct sequencing of COI gene for species identification of fish fillets sold in the Province of Buenos Aires, the largest seafood market in Argentina. Overall, 35 of the 164 (21.34%) fillet samples analyzed from seafood retailers were genetically identified as different species to the commercial name used and indicated on the product’s label. Most often, the species used as a replacement is in fact cheaper or more easily available than the one in the label (BénardCapelle et al., 2015), and could consequently be considered fraudulent. The frequency of fish mislabelling observed here is comparable to results of similar studies in Brazil (Carvalho et al., 2017), North America (Logan et al., 2008; Marko et al., 2004; Wong and Hanner, 2008), South Africa (Cawthorn et al., 2012), and Europe (Barbuto et al., 2010; Filonzi et al., 2010; Miller and Mariani, 2010; Pappalardo and Ferrito, 2015), emphasizing that this problem is pervasive on a worldwide scale. DNA sequencing studies focused upon particular fisheries have also exposed consistently high levels of mislabelling. Rockfish (Logan et al., 2008), tuna from sushi restaurants (Lowenstein et al., 2009) and red snapper (Marko et al., 2004) in the USA, cod (Miller and Mariani, 2010) and hake (Machado-Schiaffino et al., 2008) in Europe, and various threatened local species in South Africa (von der Heyden et al., 2010), all had rates of market substitution of 25% or higher. Seafood mislabelling may occur accidentally, probably linked to inaccurate morphological identification of fishes, or may be deliberate. In this work, Myliobatis goodei sold as a skate species, or Mustelus schmitii sold as Galeorhinus galeus, can be regarded as accidental mislabelling, most probably due to the resemblance between these species pairs. Confusion may also arise as species may have dissimilar vernacular names in different regions, or share a vernacular name with another species (Di Pinto et al., 2015). Furthermore, invented names are sometimes applied to species that already have a widely known common name. In the present work, 10 of the 35 mislabeled samples, were marketed with invented names that do not correspond to the official common names. For instance, “pollo de mar” was used for Squatina guggenheim, which is widely known in Argentina as “pez ángel”; “Perita” was used for identifying Menticirrhus americanus in the market, instead of the vernacular name “Burriqueta”; “Salmonada”, “Mora” and “Vacío de mar” were used for Pogonias courbina; “Palomito” was Galeorhinus galeus and “Palo rosado” was used either for Mustelus schmitii and G. galeus (Table 1). The latter case causes confusion because Mustelus schmitti was sold in the market as “Palo rosado”, which is its corresponding tradename, but the official common name is “Gatuzo”. This problem also becomes visible in cases in which different species belonging to the same genus or family, are grouped under the same common name (Barbuto et al., 2010; Cawthorn et al., 2012), reducing the importance of specific identification, such as in flounders or silversides. Our results indicate that standardization of fish market names is needed in Argentina. Species with greater number of congenerics may be more susceptible to mislabelling (Carvalho et al., 2017). The generic common names may compromise the ability of consumers to make informed choices when buying seafood (Chang et al., 2016). Consequently standard market names for seafood products are needed in order to prevent confusion. In fact, confusion in fish nomenclature has been one of the driving factors for the compilation of lists of “acceptable market names” for seafood products by many countries, such as Brazil’s
3. Results We successfully obtained 164 sequences from the 172 fillet samples acquired, representing a 95.35% success rate. In total, 28 different species were detected belonging to 22 families. Six species belonged to the group of sharks, rays and chimaeras (class Chondrichthyes) and 22 were bony fishes (class Actinopterygii). Sciaenidae had the largest number of species (six), followed by Atherinopsidae and Triakidae (two each). All other families were represented by one species each (Table 1). The length of DNA barcode sequences ranged from 492 to 654 base pairs (bp), all of which were of high quality without insertions, deletions, or stop codons. The results of the comparison with the BOLD System database generated a similarity of between 98.87% and 99.84%. Phylogenetic analysis produced cleared and well supported separation at the species level, allowing for a definitive identification (Fig. 2). On the basis of these results, 28 clusters had a BIN assignment, all of them with species assignment, in 27 genera and 22 families. In total, 35 cases of mislabeling were revealed, representing a substitution rate of 21.34% of the total. The majority of mislabeling incidents (7) were found with Thunnus albacares, which was replaced by either Callorhinchus callorynchus (once), Squatina guggenheim (once), Galeorhinus galeus (3 times) or Mustelus schmitti (2 times). Three samples were sold as Genypterus blacodes but were actually either Atlantoraja castelnaui (2 times) or M. schmitti (once). Four samples of Acanthistius patachonicus were replaced by Polyprion americanus (once), by Urophycis brasiliensis (once), Paralichthys orbignyanus (once), and by Cynoscion guatucupa (once). Other replacements were: Merluccius australis replaced by M. hubbsi (twice), Pagrus pagrus replaced by Nemadactylus bergi (once), skates by Myliobatis goodei (once), Micropogonias furnieri by 4
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Fig. 2. Molecular phylogenetic tree obtained by the neighbor joining method. The common names written in bold are those that were detected as mislabeled. MEXXX code refers to the voucher reference. The number in branches refer to the number of sequences of the same species.
(Atlantoraja castelnaui) (US$3.80) and Mustelus schmitti (US$5.50), or G. galeus (US$4.50) sold as Acanthistius patachonicus (US$9.60), or Callorhinchus callorynchus (US$4.25) and M. schmitti sold as tuna (US $7.70), or A. patachonicus as Polyprion americanus (US$12.60) (prices correspond to 1 kg of fish fillet). Something similar happens in the United States with the “white tuna” sushi sample, typically considered a more valuable sushi made from “albacore tuna”, was instead “tilapia”, a much less expensive fish (Wong and Hanner, 2008). These authors also found that the seven mislabeled “red snappers” (the accepted vernacular name for Lutjanus campechanus) were identified as belonging to five different species, each from a different genus. In fact, 77% of the fish labelled as “red snapper” in the US have been found to be substituted with less expensive and/or overexploited species (Marko et al., 2004). In South Africa, it has long been recognized that the popular and highly-valued kingklip (Genypterus capensis) is prone to market substitution, whose stocks were heavily exploited in the 1980s by an experimental longline fishery in this country and these have not since recovered to their former abundance (McLean and Glazewski, 2009). In Argentina, the most commonly consumed marine fish is the common hake, Merluccius hubbsi, whose stocks are either overexploited or depleted (Food and Agriculture Organization (FAO, 2005), and in consequence, there is a necessity for using previously underutilized species. This situation, together with the growing price of seafood products, has led to fraud with the substitution using lower-price species. The increasing trend observed in shark meat trade in many trading countries suggest that underlying demand for these products is increasing (Dent and Clarke, 2015). Thus, there are likely to be areas where demand for shark meat is sufficiently high such that, even if demand for shark fins declines, existing fishing pressure will not. South Atlantic shark populations are under intense fishing pressure (Barreto et al., 2016), given that market demands have now shifted from fins to meat. On many occasions, shark meat may represent an underutilized resource, so it is possible that markets may be turning to sharks in order to provide protein supplies, as traditional fisheries reach levels of full utilization or overutilization (Dent and Clarke, 2015). This can be clearly seen in this work, since most of the fish substitutions were made with chondrichthyan species, mainly with sharks of the family Triakidae. Both M. schmitii and G. galeus were used to replace several species such as Thunnus albacares, Cynoscion guatucupa, Genypterus blacodes and Percophis brasiliensis. In addition, these sharks were those that
Table 2 Total percentage of mislabeling distributed by cities and within each city. Cities
% over total
% per city
Mar del Plata Miramar Necochea Pinamar San Clemente Santa Teresita San Bernardo
6.13 3.68 5.52 1.84 1.84 1.23 0.61
25.64 40.00 28.13 16.67 13.64 13.33 50.00F
Ministry of Agriculture, Livestock, and Food Supply (MAPA, 2016); Canada’s Food Inspection Agency Fish List (Hanner et al., 2011), the FDA’s seafood list in the USA, and those lists compiled by member states of the European Union (Bord Iascaigh Mhara (BIM, 2001; Canadian Food Inspection Agency (CFIA, 2018; Food and Drug Administration (FDA, 2010; Food Standards Agency (FSA, 2012), in order to facilitate market regulation. Even in the case that the aforementioned mislabelling incidents were unintentional, our results highlight the need for uniform fish naming in Argentina as a means of promoting fair trade, conservation efforts and consumer rights. In Argentina there is an official list, established by the Secretaría de Políticas, Regulación e Institutos and the Secretaría de Agregado de Valor (joint resolution No. 6-E/2018; Cámara de Importadores de la República Argentina (CIRA, 2018), however this list has not been disclosed, nor is it required or its use enforced. Nevertheless, the establishment of such lists in Argentina could provide some relief to the problems of mislabelling observed in this study, and although it is unlikely that these alone will completely eliminate the problem, it would be a very important first step. A number of cases of potentially deliberate mislabelling were uncovered, where financial incentives could have been a driving factor for the substitution of highly-valued fish species with lower-valued ones. Of greater concern, is that some traders may deliberately use mislabelling as a means to launder illegally-caught fish into legitimate markets, or simply to defraud consumers for the purpose of accruing greater profits (Ogden, 2008). In the present work, there is evidence of economic fraud as higher value species were replaced by another species of lesser value, as were the cases of replacing cheeks of Genypterus blacodes (US$12) with meat of the endangered spotback skate 5
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received the greatest number of unofficial vernacular names. This replacement could be due to the lack of availability of the above mentioned species, or the low sale of sharks under their actual name, or the combination of both. The most alarming cases of replacement are those involving species classified in the highest threat categories. These cases involve the sharks G. galeus, which is considered “Critically Endangered” by the IUCN (Walker et al., 2006) and whose population trend is decreasing (Barbini et al., 2015); M. schmitii, regarded as “Endangered” (Massa et al., 2006), and S. guggenheim, also categorized as “Endangered” (Oddone et al., 2019), as well as the “Endangered” skate A. castelnaui (Hozbor et al., 2004). Conservation measures for these species include several closed areas and marine protected areas (MPA), a Maximum Allowed Catch (MAC) established by the Argentine fisheries authorities on an annual basis, and a ban on landing sharks larger than 1.6 m in total length, for commercial fishing vessels (PAN – tiburones). However, there is still much room to improve the effectiveness of these measures. Closed areas and MPAs do not cover systematically the different chondrichthyan assemblages found off Argentina (Sabadin, 2019), and MACs and the ban on landing large sharks are deficiently enforced throughout the country. Consequently, approximately 47% of Argentina’s chondrichthyan fauna is considered to be in some extinction threat category (Vulnerable, Endangered or Critically Endangered) based on an international red list of endangered species (Massa et al., 2006). Our results indicate that mislabelling may be contributing to bypassing some of the conservation measures currently in place, namely MACs and the ban on landings of large sharks. More research is needed to explore substitution rates throughout Argentina. Our findings raise considerable concern on the functioning of the fisheries supply chain in Argentina and should compel authorities to identify targets for improving labelling policies, applicable to both domestic and imported products. For example, other commercial activities, such as restaurants, may have a greater incentive to mislabel than fish retail shops due to a greater price difference of their products. Modifications to the existing legislation should, at the very least, include the requirement for the declaration on product labels of a designated ‘acceptable market name’ and scientific name of the fish species being traded. An effort should be done to create a monitoring programme at national level, to lead an intense focus on seafood certification. Obviously, such regulations will be of little value if they are not properly enforced. The government will consequently also need to address questions on whether the current regulatory monitoring activities are adequate and in cases of fraudulent acts, it should be penalized according to the amount of mislabelling detected with substantial penalties, as it has being implemented in Brazil (Carvalho et al., 2017), and might greatly reduce the incidence of market substitution (Cline, 2012). If greater transparency can be achieved on the market, then public confidence might be restored in the seafood supply chain in Argentina and full efforts may be refocused on the conservation of the ocean's fish stocks.
2010. DNA barcoding reveals fraudulent substitutions in shark seafood products: the Italian case of “palombo” (Mustelus spp.). Food Res. Int. 43, 376–381. https://doi. org/10.1016/j.foodres.2009.10.009. Barreto, R.R., de Farias, W.K.T., Andrade, H., Santana, F.M., Lessa, R., 2016. Age, growth and spatial distribution of the life stages of the shortfin mako, Isurus oxyrinchus (Rafinesque, 1810) caught in the Western and Central Atlantic. PLoS One 11 (4), e0153062. https://doi.org/10.1371/journal.pone.0153062. Bénard-Capelle, J., Guillonneau, V., Nouvian, C., Fournier, N., Le Loët, K., Dettai, A., 2015. Fish mislabelling in France: substitution rates and retail types. PeerJ 2, e714. https://doi.org/10.7717/peerj.714. Bord Iascaigh Mhara (BIM), 2001. Information Note on the Labelling of Fishery and Aquaculture Products According to Council Regulation (EC) No 104/2000 and Commission Regulation (EC) No 2065/2001. BIM, Dublin, Ireland. Bornatowski, H., Braga, R.R., Abilhoa, V., Correa, M.F.M., 2014. Feeding ecology and trophic comparisons of six shark species in a coastal ecosystem of southern Brazil. J. Fish Biol. 85, 246–263. https://doi.org/10.1111/jfb.12417. Bräutigam, A., Callow, M., Campbell, I.R., Camhi, M.D., Cornish, A.S., Dulvy, N.K., Fordham, S.V., Fowler, S.L., Hood, A.R., McClennen, C., Reuter, E.L., Sant, G., Simpfendorfer, C.A., Welch, D.J., 2015. Global Priorities for Conserving Sharks and Rays: A 2015–2025 Strategy. Cámara de Importadores de la República Argentina (CIRA), 2018. Internet Document. Accessed 20/05/2019. http://www.cira.org.ar/index.php?option=com_content& view=article&id=8142:0702-18&catid=112&Itemid=500. Canadian Food Inspection Agency (CFIA), 2018. CFIA Fish List. Internet Document. Accessed 20/05/2019. www.inspection.gc.ca/english/fssa/fispoi/product/ comnome.shtml. Carvalho, D.C., Guedes, D., Trindade, M.G., Sartori Coelho, R.M., de Lima Araujo, P.H., 2017. Nationwide Brazilian governmental forensic programme reveals seafood mislabelling trends and rates using DNA barcoding. Fish. Res. 191, 30–35. https://doi. org/10.1016/j.fishres.2017.02.021. Cawthorn, D.M., Steinman, H.A., Witthuhn, R.C., 2012. DNA barcoding reveals a high incidence of fish species misrepresentation and substitution on the South African market. Food Res. Int. 46, 30–40. https://doi.org/10.1016/j.foodres.2011.11.011. Chang, S.-C., Kung, H.-F., Chen, H.-C., Lin, C.-S., Tsai, Y.-H., 2008. Determination of histamine and bacterial isolation in swordfish fillets (Xiphias gladius) implicated in a food borne poisoning. Food Control 19, 16–21. https://doi.org/10.1016/j.foodcont. 2007.01.005. Chang, C.H., Lin, H.Y., Ren, Q., Lin, Y.S., Shao, K.T., 2016. DNA barcode identification of fish products in Taiwan: government-commissioned authentication cases. Food Control 66, 38–43. https://doi.org/10.1016/j.foodcont.2016.01.034. Chiaramonte, G., 1998. Shark fisheries in Argentina. Mar. Freshw. Res. 49 (7), 601–609. https://doi.org/10.1071/MF97136. Christiansen, H., Fournier, N., Hellemans, B., Volckaert, F.A.M., 2018. Seafood substitution and mislabeling in Brussels’ restaurants and canteens. Food Control 85, 66–75. https://doi.org/10.1016/j.foodcont.2017.09.005. Cline, E., 2012. Marketplace substitution of Atlantic salmon for Pacific salmon in Washington State detected by DNA barcoding. Food Res. Int. 45, 388–393. https:// doi.org/10.1016/j.foodres.2011.10.043. Davidson, L.N., Dulvy, N.K., 2017. Global marine protected areas to prevent extinctions. Nat. Ecol. Evol. 1https://doi.org/10.1038/s41559-016-0040. 0040. Dent, F., Clarke, S., 2015. State of the Global Market for Shark Products. FAO Fisheries and Aquaculture Technical Paper No. 590. Rome, FAO. 187 pp.. . Department of Health, South Africa (DoH), 2010. Regulations Relating to the Labelling and Advertising of Foodstuffs. Foodstuffs, Cosmetics and Disinfectants Act, 1927. Act N° 54 of 1972, GNR 146/2010. Government Printing Works, Pretoria, South Africa, pp. 1–109. de Brito, M.A., Schneider, H., Sampaio, I., Santos, S., 2015. DNA barcoding reveals highsubstitution rate and mislabeling in croaker fillets (Sciaenidae) marketed in Brazil: the case of pescada branca (Cynoscion leiarchus and Plagioscion squamosissimus). Food Res. Int. 70, 40–46. https://doi.org/10.1016/j.foodres.2015.01.031. Di Pinto, A., Marchetti, P., Mottola, A., Bozzo, G., Bonerba, E., Ceci, E., Bottaro, M., Tantillo, G., 2015. Species identification in fish fillet products using DNA barcoding. Fish. Res. 170, 9–13. https://doi.org/10.1016/j.fishres.2015.05.006. Drummond, A.J., Ashton, B., Buxton, S., Cheung, M., Cooper, A., Duran, C., Field, M., Heled, J., Kearse, M., Markowitz, S., et al., 2018. Geneious. 5.1. Internet Document. Accessed 20/05/19. http://www.geneious.com. Edgar, R.C., 2004. Muscle: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf 5, 1–19. Filonzi, L., Chiesa, S., Vaghi, M., Marzano, F.N., 2010. Molecular barcoding reveals mislabelling of commercial fish products in Italy. Food Res. Int. 43, 1383–1388. https://doi.org/10.1016/j.foodres.2010.04.016. Food and Agriculture Organization (FAO), 2005. FAO’s Report “Review of the State of World Marine Fisheries Resources. FAO Fisheries Technical Paper. No. 457. FAO, Rome 235p. Food and Agriculture Organization (FAO), 2016. The State of World Fisheries and Aquaculture. Contributing to Food Security and Nutrition for All. Rome, FAO 200p. ISBN 978-92-5-109185-2. Food and Agriculture Organization (FAO), 2018. The State of World Fisheries and Aquaculture 2018 – Meeting the Sustainable Development Goals. FAO, Rome 227p. License: CC BY-NC-SA 3.0 IGO. Food and Drug Administration (FDA), 2010. The Seafood List — FDA’s Guide to Acceptable Market Names for Seafood Sold in Interstate Commerce. Internet Document. Accessed 20/05/2019. https://www.fda.gov/regulatory-information/ search-fda-guidance-documents/cpg-sec-540750-common-or-usual-names-seafoodinterstate-commerce. Food Standards Agency (FSA), 2012. Table of Commercial Designations for Specific Fish
Acknowledgements We would like to thank to D. Castellini, C. Spath and G. Blasina who collaborated in the samplings; and the anonymous referees for the suggestions. Funding was provided by Universidad Nacional de Mar del Plata (EXA 867/18), and CONICET by means of the iBOL Reference laboratory. References Ardura, A., Planes, S., Garcia-Vazquez, E., 2011. Beyond biodiversity: fish metagenomes. PLoS One 6 (8), e22592. https://doi.org/10.1371/journal.pone.0022592. Barbini, S.A., Lucifora, L.O., Figueroa, D.E., 2015. Using opportunistic records from a recreational fishing magazine to assess population trends of sharks. Can. J. Fish. Aquat. Sci. 72 (12), 1853–1859. https://doi.org/10.1139/cjfas-2015-0087. Barbuto, M., Galimberti, A., Ferri, E., Labra, M., Malandra, R., Galli, P., Casiraghi, M.,
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Fisheries Research 221 (2020) 105373
G. Delpiani, et al.
McLean, B., Glazewski, J.I., 2009. Marine environments. In: Strydom, H.A., King, N.D. (Eds.), Fuggle & Rabie’s Environmental Management in South Africa, 2nd ed. Juta, South Africa, pp. 455–511. Meier, R., Zhang, G., Ali, F., 2008. The use of mean instead of smallest interspecific distances exaggerates the size of the “Barcoding Gap” and leads to misidentification. Syst. Biol. 57 (5), 809–813. https://doi.org/10.1080/10635150802406343. Meyer, C.P., Paulay, G., 2005. DNA Barcoding: error rates based on comprehensive sampling. PLoS Biol. 3 (12), e422. https://doi.org/10.1371/journal.pbio.0030422. Miller, D.D., Mariani, S., 2010. Smoke, mirrors, and mislabeled cod: poor transparency in the European seafood industry. Front. Ecol. Environ. 8, 517–521. https://doi.org/10. 1890/090212. Oddone, M., Awruch, C.A., Barreto, R., Charvet, P., Chiaramonte, G.E., Cuevas, J.M., Dolphine, P., Faria, V., Paesch, L., Rincon, G., Vooren, C.M., 2019. Squatina guggenheim. The IUCN Red List of Threatened Species 2019. e.T130393378A130393975. https://doi.org/10.2305/IUCN.UK.2019-1.RLTS.T130393378A130393975.en. Ogden, R., 2008. Fisheries forensics: the use of DNA tools for improving compliance, traceability and enforcement in the fishing industry. Fish Fish. 9, 462–472. https:// doi.org/10.1111/j.1467-2979.2008.00305.x. Pappalardo, A.M., Ferrito, V., 2015. DNA barcoding species identification unveils mislabeling of processed flatfish products in southern Italy markets. Fish. Res. 164, 153–158. https://doi.org/10.1016/j.fishres.2014.11.004. Pepe, T., Trotta, M., Di Marco, I., Anastasio, A., Bautista, J.M., Cortesi, M.L., 2007. Fish species identification in surimi-based products. J. Agric. Food Chem. 55, 3681–3685. https://doi.org/10.1021/jf063321o. Ratnasingham, S., Hebert, P.D.N., 2013. A DNA-based registry for all animal species: the Barcode Index Number (BIN) system. PLoS One 8 (7), e66213. https://doi.org/10. 1371/journal.pone.0066213. Sabadin, D.E., 2019. Patrones de distribución geográfica de la biodiversidad de tiburones, rayas y quimeras (Chondrichthyes) del Atlántico Sudoccidental. PhD Thesis. Universidad Nacional de Mar del Plata, Mar del Plata. Stoeckle, M., Waggoner, P., Ausubel, J., 2004. Barcoding Life: Ten Reasons. Consortium for the Barcode of Life (CBOL). Internet Document. Accessed 20/05/2019. www. barcoding.si.edu. Tokeshi, M., Arakaki, S., Daud, J.R.P., 2013. Consuming diversity: analysis of seasonal catch patterns in multispecies artisanal reef fisheries in North Sulawesi, Eastern Indonesia. Pac. Sci. 67 (1), 1–13. https://doi.org/10.2984/67.1.1. Vallianos, C., Sherry, J., Hofford, A., Baker, J., 2018. Sharks in Crisis: Evidence of Positive Behavioral Change in China as New Threats Emerge. Wildaid. Internet document, https://wildaid.org/wp-content/uploads/2018/02/WildAid-Sharks-in-Crisis-2018. pdf. Accessed 20/05/2019. Veneza, I., Da Silva, R., Freitas, L., Silva, S., Martins, K., Sampaio, I., Schneider, H., Gomes, G., 2018. Molecular authentication of Pargo fillets Lutjanus purpureus (Perciformes: Lutjanidae) by DNA barcoding reveals commercial fraud. Neotrop. Ichthyol. 16, e170068. https://doi.org/10.1590/1982-0224-20170068. von der Heyden, S., Barendse, J., Seebregts, A.J., Matthee, C.A., 2010. Misleading the masses: detection of mislabelled and substituted frozen fish products in South Africa. ICES J. Mar. Sci. 67, 176–185. https://doi.org/10.1093/icesjms/fsp222. Walker, T.I., Cavanagh, R.D., Stevens, J.D., Carlisle, A.B., Chiaramonte, G.E., Domingo, A., Ebert, D.A., Mancusi, C.M., Massa, A., McCord, M., Morey, G., Paul, L.J., Serena, F., Vooren, C.M., 2006. Galeorhinus galeus. The IUCN Red List of Threatened Species 2006. Internet document, https://doi.org/10.2305/IUCN.UK.2006.RLTS. T39352A10212764.en. e.T39352A10212764 Accessed 20/05/2019. . Wong, E.H.K., Hanner, R.H., 2008. DNA barcoding detects market substitution in North American seafood. Food Res. Int. 41, 828–837. https://doi.org/10.1016/j.foodres. 2008.07.005. Yancy, H.F., Zemlak, T.S., Mason, J.A., Washington, J., Tenge, B., Nguyen, N.–L., Barnett, J.D., Savary, W.E., Hill, W.E., Moore, M.M., Fry, F.S., Randolph, S.C., Rogers, P.L., Hebert, P.D., 2008. Potential use of DNA barcodes in regulatory science: applications of the Regulatory Fish Enciclopedia. J. Food Protect. 71, 210–217. https://doi.org/ 10.4315/0362-028X-71.1.210.
Species Derived From the Fish Labelling (England) Regulations 2010. Internet Document. Accessed 20/05/2019. https://assets.publishing.service.gov.uk/ government/uploads/system/uploads/attachment_data/file/82592/20120918-fishlabelling-consult-reg-guide-note.pdf. Handy, S.M., Deeds, J.R., Ivanova, N.V., Hebert, P.D., Hanner, R.H., Ormos, A., Weight, L., Moore, M.M., Yancy, H.F., 2011. A single laboratory validated method for the generation of DNA barcodes for the identification of fish for regulatory compliance. J. AOAC Int. 94, 201–210 https://doi.org/101093/bib/bbq004. Hanner, R., Becker, S., Ivanova, N.V., Steinke, D., 2011. FISH-BOL and seafood identification: geographically dispersed case studies reveal systemic market substitution across Canada. Mitochondrial DNA 22 (Suppl.1), 106–122. https://doi.org/10.3109/ 19401736.2011.588217. Hebert, P.D.N., Cywinska, A., Ball, S.L., deWaard, J.R., 2003. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10. 1098/rspb.2002.2218. Hozbor, N., Massa, A., Vooren, C.M., 2004. Atlantoraja castelnaui. The IUCN Red List of Threatened Species 2004. e.T44575A10921544. https://doi.org/10.2305/IUCN.UK. 2004.RLTS.T44575A10921544.en. Hsieh, H.-s., Chai, T.-j., Hwang, D.F., 2007. Using PCR-RFLP method to identify the species of different processed products of billfish meats. Food Control 18, 369–374. https://doi.org/10.1016/j.foodcont.2005.11.002. Ivanova, N.V., Dewaard, J., Hanner, P.D.N., 2006. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Mol. Ecol. Notes 6, 998–1002. https://doi. org/10.1111/j.1471-8286.2006.01428.x. Ivanova, N.V., Zemlak, T.S., Hanner, R.H., Hebert, P.D., 2007. Universal primer cocktails for fish DNA barcoding. Mol. Ecol. Notes 7, 544–548. https://doi.org/10.1111/j. 1471-8286.2007.01748.x. Kimura, M., 1980. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120. https://doi.org/10.1007/BF01731581. Kumar, S., Stecher, G., Tamura, K., 2016. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33 (7), 1870–1874. https:// doi.org/10.1093/molbev/msw054. Lassmann, T., Frings, O., Sonnhammer, E.L., 2009. Kalign2: high-performance multiple alignment of protein and nucleotide sequences allowing external features. Nucleic Acids Res. 37 (3), 858–865. https://doi.org/10.1093/nar/gkn1006. Logan, C.A., Alter, S.E., Haupt, A.J., Tomalty, K., Palumbi, S.R., 2008. An impediment to consumer choice: overfished species are sold as Pacific red snapper. Biol. Conserv. 141, 1591–1599. https://doi.org/10.1016/j.biocon.2008.04.007. Lowenstein, J.H., Amato, G., Kolokotronis, S.-O., 2009. The real maccoyii: identifying tuna sushi with DNA barcodes - contrasting characteristic attributes and genetic distances. PLoS One 4, e7866. https://doi.org/10.1371/journal.pone.0007866. Machado-Schiaffino, G., Martinez, J.L., Garcia-Vazquez, E., 2008. Detection of mislabelling in hake seafood employing mtSNPs-based methodology with identification of eleven hake species of the genus Merluccius. J. Agric. Food Chem. 56, 5091–5095. https://doi.org/10.1021/jf800207t. MAPA – Ministry of Agriculture, Livestock and Food Supply, 2016. Instrucão Normativa n°29, de 23 de setembro de 2015. Mariani, S., Griffiths, A.M., Velasco, A., Kappel, K., Jérôme, M., Perez-Martin, R.I., Schröder, U., Verrez-Bagnis, V., Silva, H., Vandamme, S.G., Boufana, B., Mendes, R., Shorten, M., Smith, C., Hankard, E., Hook, S.A., Weymer, A.S., Gunning, D., Sotelo, C.G., 2015. Low mislabeling rates indicate marked improvements in European seafood market operations. Front. Ecol. Environ. 13 (10), 536–540. https://doi.org/10. 1890/150119. Marko, P.B., Lee, S.C., Rice, A.M., Gramling, J.M., Fitzhenry, T.M., McAlister, J.S., Harper, G.R., Moran, A.L., 2004. Mislabelling of a depleted reef fish. Nature 430, 309–310. https://doi.org/10.1038/430309b10.1038/430309b. Massa, A., Hozbor, N., Chiaramonte, G.E., Balestra, A.D., Vooren, C.M., 2006. Mustelus schmitti. The IUCN Red List of Threatened Species 2006. https://doi.org/10.2305/ IUCN.UK.2006.RLTS.T60203A12318268.en. Internet document, e. T60203A12318268 Accessed 20/05/2019.
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