Abstract
Background and aims. The Fish Barcode of Life campaign involves a broad international collaboration among scientists working to advance the identification of fishes using DNA barcodes. With over 25% of the world's known ichthyofauna currently profiled, forensic identification of seafood products is now feasible and is becoming routine.
Materials and methods. Driven by growing consumer interest in the food supply, investigative reporters from five different media establishments procured seafood samples (n = 254) from numerous retail establishments located among five Canadian metropolitan areas between 2008 and 2010. The specimens were sent to the Canadian Centre for DNA Barcoding for analysis. By integrating the results from these individual case studies in a summary analysis, we provide a broad perspective on seafood substitution across Canada.
Results. Barcodes were recovered from 93% of the samples (n = 236), and identified using the Barcode of Life Data Systems “species identification” engine (www.barcodinglife.org). A 99% sequence similarity threshold was employed as a conservative matching criterion for specimen identification to the species level. Comparing these results against the Canadian Food Inspection Agency's “Fish List” a guideline to interpreting “false, misleading or deceptive” names (as per s 27 of the Fish Inspection regulations) demonstrated that 41% of the samples were mislabeled. Most samples were readily identified; however, this was not true in all cases because some samples had no close match. Others were ambiguous due to limited barcode resolution (or imperfect taxonomy) observed within a few closely related species complexes. The latter cases did not significantly impact the results because even the partial resolution achieved was sufficient to demonstrate mislabeling.
Conclusion. This work highlights the functional utility of barcoding for the identification of diverse market samples. It also demonstrates how barcoding serves as a bridge linking scientific nomenclature with approved market names, potentially empowering regulatory bodies to enforce labeling standards. By synchronizing taxonomic effort with sequencing effort and database curation, barcoding provides a molecular identification resource of service to applied forensics.
Introduction
The intentional mislabeling of seafood with a product of lesser value constitutes a growing form of economic adulteration that is of concern for fisheries resource management worldwide (Jacquet and Pauly Citation2008). Reasons for substitution include high demand with limited supply, high profit incentive, an increase in international trade of processed foods, and lack of regulation enforcement and implementation (Miller and Mariani Citation2010). Seafood products have been found mislabeled at high levels in North America and Europe. For example, DNA-based approaches have demonstrated that between 60 and 94% of fishes labeled as Red Snapper Lutjanus campechanus (Poey) for sale in the USA were mislabeled (Marko et al. Citation2004), as was 25% of various species obtained from markets and restaurants in New York City (USA) and Toronto (Canada) (Wong and Hanner Citation2008). In Ireland, a substitution rate of 25% was revealed among cod and haddock products, and this increased to 82% among smoked fish samples (Miller and Mariani Citation2010). A similar substitution rate of close to 80% was also found in shark seafood products in Italy (Barbuto et al. Citation2010). According to Caddy and Garibaldi (Citation2000) only 65% of worldwide fishery captures reported to FAO for the year 1996 were identified at the species level, ranging from about 90% in temperate areas to less than 40% in tropical regions. High levels of substitution occur due to insufficient identification and inspection capacities, and can lead to misrepresentation of sustainable fisheries, such as those certified by the Marine Stewardship Council (Jacquet et al. Citation2010). This situation exposes retailers and consumers to cases of fraud, and poses a health risk in certain cases, such as those involving puffer fish (Cohen et al. Citation2009) and escolar (Lowenstein et al. Citation2009). Not only does this situation undermine consumer confidence in fish and seafood products, it places honest local or domestic producers at a disadvantage when fraudulent suppliers or importers undercut their margins through substitution, overfishing, or disobedience of fisheries regulations. Another prevalent concern is that mislabeling discounts the efforts of conscientious consumers in upholding conservation prohibitions (Logan et al. Citation2008; Wong and Hanner Citation2008).
DNA barcoding is a molecular method that utilizes the mitochondrial 5′ region of the cytochrome c oxidase subunit I (COI) gene for animal identification (Hebert et al. Citation2003). The method has been used very successfully to discriminate both marine fishes (e.g. Ward et al. Citation2005) and freshwater fishes (e.g. Hubert et al. Citation2008). The need for comprehensive and reliable species identification tools combined with early barcoding success among taxonomically diverse fishes led to the foundation of the Fish Barcode of Life (FISH-BOL) initiative (http://www.fishbol.org). FISH-BOL has the primary goal of gathering DNA barcode records for all the world's fishes, about 31,000 species (Ward et al. Citation2009). By April 2011 more than 8100 species have been barcoded, with greater than 25% coverage reached (Becker et al. Citation2011). The Barcode of Life Data Systems (BOLD, www.boldsystems.org; Ratnasingham and Hebert Citation2007), adopted by FISH-BOL, provides a sophisticated platform for DNA barcode data storage, management, and includes species identification tools.
DNA barcoding can be used to identify specimens including whole fish, fillets, fins, juveniles, larvae, eggs, or tissue fragments. It is recognized by the Food and Drug Administration in the USA as a replacement for the time-consuming technique of protein isoelectric focusing for fish identification (Yancy et al. Citation2008; Handy et al. Citation2011) and can be applied to raw, cooked (Wong and Hanner Citation2008), or smoked fish (Smith et al. Citation2008; Miller and Mariani Citation2010). It also has the potential to be used with heavily processed food samples by using short mini barcode regions (reviewed in Rasmussen et al. (Citation2009)), and the reference library aids construction of molecular probes based on short, species-specific patterns of variation found in the standard barcode sequence (Eytan and Hellberg Citation2010; Rasmussen et al. Citation2010). The potential of DNA barcoding to provide unequivocal species assignments from whole or partial specimens may significantly reform seafood market practices, particularly for commercially important species (Rasmussen et al. Citation2009).
The objective of the present study was to assess the extent to which market names conform to the accepted trade names for seafood [as established by the Canadian Food Inspection Agency (CFIA)] by analyzing the results of several ad hoc surveys conducted in collaboration with various media outlets across Canada between 2008 and 2010. Given an estimated annual impact of US$240 billion from fisheries world-wide (Dyck and Sumaila Citation2010), the socioeconomic impact of seafood fraud deserves both public exposure and scientific documentation. Scientific names were obtained by matching specimen barcodes against reference sequence libraries from BOLD and GenBank. These were compared with the relevant species name(s) corresponding to the recorded market name as derived from the CFIA Fish List (http://www.inspection.gc.ca/english/fssa/fispoi/product/comnome.shtml).
Materials and methods
Seafood samples
A total of 254 seafood samples were purchased from various retailers, takeouts, and restaurants from 2008 to 2010 from five Canadian metropolitan areas, including Vancouver, Toronto, Gatineau, Montreal, and Quebec. Sample acquisition strategies for fillets generally targeted broad taxonomic coverage and diverse vendor coverage within each area, while sampling from restaurants and takeouts often targeted species known to be commonly substituted. Specimens were purchased, with tissues subsampled for analysis and preserved by freezing. All specimen provenance data, including the market name, were recorded locally. This work was carried out by investigative reporters from the Canadian Broadcasting Corporation (CBC Marketplace), Radio Canada (L'épicerie TV show), The Vancouver Sun newspaper, Montreal CTV TV station, and The Toronto Star newspaper. Tissues were then repacked in neutral packaging material and labeled with a neutral sample ID only and shipped frozen to the Canadian Centre for DNA Barcoding for sample-blind molecular forensic analysis. A detailed overview of the samples is given in .
Table I. Sample overview.
DNA extraction
Upon arrival at the Canadian Centre for DNA Barcoding, frozen market samples were subsampled in a sterile flow hood. About 2 mm3 tissue was extracted from the inside of each frozen sample, using tools that were treated with ELIMINase (Decon Laboratories, USA) between subsampling of different specimens. Each tissue piece was placed in a single-plate well with 30 μl of 95% ethanol for preservation. Genomic DNA was subsequently extracted with a membrane-based approach on a Biomek FX liquid handling station (Beckman Coulter, USA) using AcroPrep 96 1.0 ml filter plates with 1.0 μm PALL glass fiber media (Ivanova et al. Citation2006). To be able to generate a barcode from as many specimens as possible, 46 fillet samples that failed to generate a barcode after a first round of standard PCR and sequencing were later subsampled again as described above, and genomic DNA from these samples was extracted manually using the DNeasy Blood and Tissue kit (Qiagen, USA). In this manual processing, the tissue pieces were first incubated (under constant shaking at 300 rpm) overnight at 56°C in 180 μl tissue lysis buffer ATL and 20 μl proteinase K, followed by DNA extraction and final elution in 200 μl elution buffer, according to the manual of the Qiagen kit.
PCR amplification and sequencing
A 652 bp fragment of the standard COI DNA barcode region was amplified using either a fish and/or mammal PCR primer cocktail appended with M13 (Messing Citation1983) tails to aid in a standard sequencing protocol (); see Ivanova et al. (Citation2007) for details. The first PCR round was carried out with the fish PCR primer cocktail. A second attempt with the mammal cocktail was undertaken when the first attempt at PCR did not result in successful amplification or sequencing. When this approach was unsuccessful, genomic DNA was re-extracted manually from the samples affected (see above), and PCR was then repeated with the two primer cocktails described above. Each PCR mixture consisted of 6.25 μl of 10% trehalose, 2 μl of ultrapure ddH2O, 1.25 μl of 10 × PCR buffer for Platinum Taq (Invitrogen, Inc., USA), 0.625 μl of 50 mM MgCl2, 0.125 μl of 10 μM primer cocktail (see ), 0.0625 μl of 10 mM dNTP mix, 0.06 μl of Platinum Taq polymerase (Invitrogen), and 2.0 μl of template DNA in 12.5 μl of total reaction volume. PCR amplification reactions were conducted on Eppendorf Mastercycler ep gradient thermal cyclers (Brinkmann Instruments, USA). The thermocycling program consisted of a hot start of 94°C for 1 min; followed by five cycles of 94°C for 30 s, 50°C for 40 s, 72°C for 1 min; then 35 cycles of 94°C for 30 s, 54°C for 40 s, 72°C for 1 min; then an extension of 72°C for 10 min, and finally held at 4°C. PCR products were visualized on 2% agarose E-gel 96 plates (Invitrogen) stained with ethidium bromide. PCR samples with an abundant single band were bi-directionally sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems Inc. (ABI), USA). Each forward or reverse cycle sequencing reaction mixture consisted of 0.25 μl of BigDye (Applied Biosystems Inc.), 1.875 μl of 5 × buffer (400 mM Tris–HCl, pH 9.0, 10 mM MgCl2), 5 μl of 10% trehalose, 1.0 μl of primer (10 μM; M13F or M13R, ), 0.875 μl of ultrapure ddH2O, and 1.5 μl of PCR product. The sequencing reaction thermocycling program consisted of 2 min at 96°C, followed by 30 cycles of 30 s at 96°C, 15 s at 55°C, and 4 min at 60°C, followed by a hold at 4°C. Fluorescent signals were recorded on an ABI 3730 DNA analyzer.
Table II. PCR and sequencing primers used in the present study.
Data evaluation and interpretation
Bi-directional sequences were assembled and edited using CodonCode Aligner software (CodonCode Corporation, USA). All sequence contig assemblies are provided in the supplementary Figure S1. We analyzed the DNA barcode sequences derived from unknown samples with the “species”-level identification function of the BOLD ID Engine (version April 2011). A top species match was identified with a sequence similarity of at least 99%, and the results were double-checked via BLAST searches of the GenBank database. Species identifications for each specimen were compared with the relevant species name(s) corresponding to the recorded market name as derived from the CFIA Fish List (http://www.inspection.gc.ca/english/fssa/fispoi/product/comnome.shtml) (version April 2011). The criterion for the identification of potentially mislabeled samples was based solely on the literal information of acceptable species that can be sold under a given common market name (based on a strict interpretation of the CFIA Fish List) versus the name of the species inferred by barcoding. Therefore, some cases of mislabeled fish are potentially less egregious than others, and might not be considered surprising given common consumer knowledge and expectations. However, this literal approach was used to ensure that the determination of potential mislabeling was conducted consistently for all samples, which was particularly important in cases where a single market name is applied to multiple species, or multiple names across various name categories existed for a species (e.g. common, market, and vernacular names). An example of mislabeling is “Pacific Salmon”, because in Canada all Pacific Salmon must include a species' common name (see: http://www.inspection.gc.ca/english/fssa/fispoi/commun/20101220e.shtml). Hence, irrespective of DNA testing, some specimens are mislabeled simply because the label failed to conform to an accepted common name. However, mislabeling also relates to market substitution in cases when an accepted common name (as indicated on the label or menu) failed to match the expected species identity as revealed by barcoding. Examples are the substitution of Red Snapper with Tilapia, or Coho Salmon with Atlantic Salmon. Thus, all cases of “substitution” are considered mislabeled (see ).
Results and discussion
For the present study, 254 fish and seafood samples were collected at retail outlets, takeouts, and restaurants in five Canadian metropolitan areas including Vancouver, Toronto, Gatineau, Montreal, and Quebec. Among these samples, 236 (93.3%) yielded high-quality sequences with a length of at least 418 bp. The observed failure rate of 6.7% () is similar to other fish barcoding studies (e.g. Ward et al. Citation2009; Nwani et al. Citation2011). For those samples that yielded a barcode, we used the BOLD “species-level” identification tool to query the sample barcode against the reference database (details in Materials and methods), which provided a sequence similarity value greater than 99% for 230 of the 236 samples with a barcode (). The remaining six samples exhibited no significant match to anything in the database. Of the 230 samples with a match, 195 samples were unambiguously assignable to a single species (). However, 35 samples were unresolved (e.g. sample CBCMS046-10 of Thunnus sp.) in cases where two or more sister species appear to share a barcode in the reference library. For example, the ability to discriminate closely related species of Thunnus is problematic (Chow and Kishino Citation1995); and this is true even for barcoding (Viñas and Tudela Citation2009; but see Lowenstein et al. Citation2009). Because different researchers have used different sets of reference sequences and because voucher specimens are lacking for these sequences, it is impossible to validate the identifications assigned to the reference sequences in question and thereby resolve conflicts within or between studies. Hence, some level of ambiguity exists in the accuracy of the underlying taxonomic identifications. Alternatively, closely related species may be in a state of incomplete lineage diversification where they retain ancestral polymorphisms, occasionally hybridize, or both. While this is the exception rather than the norm, certain taxonomically challenging species complexes involving genera such as Salvelinus and Sebastes include closely related sister species that appear to share barcodes. This is not surprising because hybridization or incomplete lineage sorting of ancestral polymorphisms has been documented previously in Sebastes (Steinke et al. Citation2009) and Salvelinus (Baxter et al. Citation1997). While identification could only be resolved to a congeneric species complex in such cases (see footnotes for ), this level of resolution was still sufficient to detect gross substitution ().
Table III. Summary results for 236 seafood samples barcoded (out of 254 samples submitted) from across Canada.
Taxonomic misidentification of reference specimens complicates the resolution of apparent haplotype sharing, yet cleansing the reference database of such identification errors requires a concerted effort that often includes both the original specimen collector, and in some cases additional experts who can provide an independent identification of the specimens that represent outliers in the database. Most species are represented by a single, cohesive cluster of barcodes that do not overlap with any other named species () and these patterns are typically reinforced with the accumulation of additional reference sequences derived from independent sources. However, discrepancies do arise and annotating the library accordingly is an ongoing process. Besides, vetting voucher identifications, critical points for data curation include ranking taxonomic identifications using a quality metric (Steinke and Hanner Citation2011), as well as considering the proximity of barcoded specimens to the type locality of that species (Lowenstein et al. Citation2011) and flagging for removal from the ID engine those outliers that appear to be contaminants or whose identifications cannot be substantiated. The barcode-based identification of poorly sampled species represents a challenge for forensics (Wilson-Wilde et al. Citation2010), where interpretation of matches may require expert opinion. However, barcoding is making species-level identifications more accessible through BOLD, despite some conflicts. Indeed, the database has been proven very reliable in this study (>99% similarity for most samples tested; ). Ultimately, regulatory agencies require full transparency and traceability for any sequences that are used in regulatory decisions. Barcoding and BOLD support these objectives and provide a platform for data curation, but curatorial annotation practices must be implemented to circumscribe the expert interpretation of conflicting data. Otherwise, the database will lose functionality as a trusted identification resource.
Out of the 236 samples with a DNA barcode, 41.2% (n = 89) were found mislabeled (). provides an overview of the mislabeling rates detected in different metropolitan areas and venues therein (e.g. fillet purchased from the market versus meal purchased in a restaurant or takeout). Cases of fillet mislabeling do not significantly differ across the various localities examined. However, the combined incidence of restaurant and takeout mislabeling is significantly greater than that of market fillet mislabeling (p>0.001 based on a conditional chi-square test with one degree of freedom). This could be due to a sampling bias, however, because samples collected from sushi restaurants were directed toward species that are known targets of substitution (e.g. red snapper). Non-standardized sample procurement represents an acknowledged shortcoming when performing a retrospective analysis of multiple case studies, yet the pervasive nature of substitution and mislabeling are consistently evident across Canada. Our results are in concert with related studies (e.g. Wong and Hanner Citation2008; Miller and Mariani Citation2010), which taken collectively clearly illustrate that seafood mislabeling is widespread and common.
In our study, cod was often substituted (e.g. with Melanogrammus aeglefinus or Gadus chalcogrammus), as was Red Snapper. The Pacific Salmon samples often lacked required species designation (e.g. Coho, Sockeye, Pink), and were also sometimes substituted with Atlantic Salmon. Halibut was not always correctly labeled as Atlantic versus Pacific. In several cases, Patagonian Toothfish was called Chilean Seabass, a vernacular name that is commonly used despite not being listed as an acceptable market name. Other notable findings include sample CBCMS049-10, labeled as “shark steak” and subsequently identified as Carcharhinus plumbeus (Sandbar Shark). Neither the common name nor the species are currently included in the CFIA Fish List. The International Union for the Conservation of Nature classifies it as vulnerable globally, with significant declines estimated and suspected in several areas of its range, (see: http://www.iucnredlist.org/apps/redlist/details/3853/0). However, it should be noted that the so-called C. plumbeus reference sequences are scattered across several distinct haplogroups in BOLD.
This highlights several issues. First, only about one-half of the known species of elasmobranchs have been barcoded (Becker et al. Citation2011), and since there are only a few shark vouchers for those that have been profiled (because of body size constraints with respect to archival), some of these clusters may represent cryptic species, misidentifications, and/or other described species that are not yet clearly identified within the reference database. Most known shark species are well represented and exhibit cohesive barcode clusters that are distinct from those of other known species, but the dataset needs further expansion to include more expert-identified reference material. While FISH-BOL/BOLD attempts to use such material, it also retrieves data from GenBank, which is not actively curated and is also known to contain inaccuracies (Harris Citation2003). BOLD does provide a platform to support third-party annotation of suspicious GenBank records and flag them for removal from the BOLD identification engine, but in some cases, there are not enough comparative data to make a clear decision. Second, based on currently unresolved entries in the reference database, there is a slim possibility that the sample might actually be Carcharhinus altimus (Bignose Shark). Notably, this species is not included in the CFIA Fish List either. Third, assuming current taxonomic classification to be accurate, the use of additional markers might be necessary to discern certain closely related shark species (e.g. Wong et al. Citation2009). These issues notwithstanding, for species such as the Sandbar Shark to be sold in Canadian markets, the industry needs to petition the CFIA to include them under an accepted common name in the Fish List. Until this happens, these species should not be entering the human food supply.
Concluding remarks
For consumers, this work illuminates the problem of market substitution and provides an important example of “translational taxonomy” as enabled by barcoding. While a prior forensic study on commercial fish and seafood products in North America uncovered taxonomically widespread mislabeling (Wong and Hanner Citation2008), this research significantly extends the geographic coverage and depth of sampling over previous investigations, exposing systemic seafood mislabeling across much of Canada. Limited sample sizes and an ad hoc sampling schema hinder us from making inferences about relative rates of mislabeling between species, while sampling biases restrict inferences concerning baseline substitution across the entire economic spectrum of available seafood options as a whole. Hence, we cannot explicitly test the hypothesis that inexpensive species are less commonly mislabeled than more expensive species. Yet, we suspect that the substitution of inexpensive species is much less common than the substitution levels observed among the higher priced species exposed in this study. A more balanced experimental design that incorporates samples from across all seafood price categories, regions, species, and major importers would certainly prove informative. A conservative worldwide substitution rate of just 10% (as detected in the fillet samples from this study) would implicate US$S24 billion (10% of US$S240 billion cf. Dyck and Sumaila Citation2010) in fraudulent seafood shipped annually worldwide, a statistic that when combined with substitution levels as documented in this study should provoke more thorough investigations.
While demonstrating the utility of barcoding, we also highlight taxonomic uncertainty pertaining to some poorly resolved and/or potentially cryptic taxa, thereby flagging targets for further taxonomic inquiry. Although most species of commercial interest are already well characterized both traditionally and more recently with barcodes, more work remains for constructing the reference library as evidenced by the six samples that yielded no close match in either BOLD or GenBank. Regulatory lists serve as “Rosetta Stones” for linking common, market, scientific, and vernacular names and, when combined with DNA barcoding, an innovative solution for detecting and controlling market substitution emerges (Yancy et al. Citation2008; Handy et al. Citation2011). This approach should also be seen as an important adjunct to popular certification schemes currently under scrutiny (e.g. Jacquet et al. Citation2010). Moreover, the uptake of barcode-based approaches for species identification could protect both consumers and retailers from market fraud (and any liabilities this might incur) as well as aid implementation of conservation legislation and catchment monitoring on a global scale.
One unintended consequence of improved monitoring empowered by barcoding could be the elimination of Tilapia substitutions commonly seen in the market, resulting in greater pressure on already depleted stocks of some marine species. Consumer education is crucial and in this respect, we highlight the role of responsible journalism in aiding consumers to make informed choices—not only with respect to economic fraud, but also concerning ethical consumption. The need for accurate and transparent labeling exists not only for species, but also extends to country of origin and capture method if consumers are to be fully capable of exerting market pressures in favor of sustainability.
Supplementary Material
Download MS Word (195.5 KB)Acknowledgements
The authors dedicate this paper to the investigative reporters who helped make it possible and the quality journalism they produced in relation to the individual case studies. The authors highlight the key role that ethical reporting plays in raising consumer awareness and driving social change. They are very grateful to Greg Sadler (Canadian Broadcasting Corporation, CBC Marketplace), Alain Roy (L'épicerie Société Radio Canada), Larry Pynn (The Vancouver Sun newspaper), Amy Luft (Montreal CTV TV station), and Susan Sampson (The Toronto Star newspaper) for sample collection and shipping. Permission has been given by these organizations to publish the data derived from their samples. They offer special thanks to Jake Lowenstein for a stimulating review and Teri Crease for a helpful discussion on the use of the contingency test. The authors acknowledge Jonathan Deeds, Sergios-Orestis Kolokotronis, Rosalee Hellberg and David Schindel for comments on an earlier version of this manuscript. We also offer special thanks to Jason Agius who provided the following comments and clarifications as this article was going to press: 1) the CFIA recently issued an “Industry Notice” regarding the labeling of Pacific Salmon [http://www.inspection.gc.ca/english/fssa/fispoi/commun/20101220e.shtml]; 2) according to the Fish List “halibut” would be acceptable for both Atlantic or Pacific halibut and the geographic name of harvest “Alaskan Halibut” would be considered acceptable as well [see: http://www.inspection.gc.ca/english/fssa/fispoi/product/comnome.shtml & http://www.inspection.gc.ca/english/fssa/labeti/guide/ch4ae.shtml#a4_20 (4.20.1 Geographical terms)]; 3) neither Seriola lalandi nor Seriola quinqueradiata are on the Fish List, but Seriola sp. is included for Amberjack and the Fish List should be amended accordingly; 4) similarly, Oreochromis aureus is not yet on the Fish List; while 5) Pagrus major is also known as Silver Seabream, Japanese Seabream or Genuine porgy on the Fish List. Daniel Carvalho (Universidade Federal de Minas Gerais), Heather Braid, Suresh Naik and Evgeny Zakharov (University of Guelph) aided work in the laboratory. The BOLD informatics team provided efficient database support. The CCDB acknowledges support from Genome Canada (through the Ontario Genomics Institute), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Ontario Ministry of Research and Innovation (MRI).
Declaration of interest: The authors report no conflicts of interest. The authors contributed equally to the manuscript and they alone are responsible for the content and writing of the paper. D.S. was supported by funding from the Alfred P. Sloan Foundation to MarBOL. S.B. was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC).
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