Unsupported Conclusions on Net Conservation Benefits of Mislabeling Seafood
Notice bibliographique
Résumé
Stawitz et al. have attempted to quantifiably address the impacts of seafood mislabeling in a powerful statistical fashion, an admirable goal. However, their conclusion that mislabeling increases conservation status of consumed items is not well supported. Stawitz et al.’s (2016) analyses and conclusions on conservation were particularly troublesome given their nontransparent methods. The authors did not reveal which 43 studies of the 45 listed in Table S1 met their criteria for meta-analyses, nor did they explain why they included 5,200 non-mislabeled samples in their analyses. They did not provide the proportion of the approximately 1,553 (i.e., 6,754/0.23) “true id” or “labeled” samples subjected to estimated IUCN conservation status nor explain how the sizable proportion of aquacultured species were treated. No reasoning was given for why they performed their own de novo investigation of mislabeling globally using U.S. guidance on seafood market names (figure S1). Given the vagaries of seafood labeling globally, we are not convinced that quantifying the conservation net benefits of mislabeling is an appropriate research question. The authors’ IUCN averaging method ignores the conservation and health implications of vaguely labeled seafood (e.g., Lowenstein et al. 2010; Lamedin et al. 2015) and prevents robust comparisons of conservation status between labeled and true id samples. We envision a number of potential erroneous conclusions based on the vagueness of the label, the nonspecificity of the true id, and the contrasting conservation ranking of disparate species within one genus (e.g., Thunnus, Supplementary Table provided). The incongruity of the Food and Agriculture Organization of the United Nations (FAO) and RAM Legacy Stock Assessment Database (RAM) database results (supp. S7 & S8) further weakens conclusions based on presumed IUCN status changes by showing larger numbers of true id genera that change status, and in opposite directions compared to IUCN trends (e.g., grouper, flounder, and Atlantic salmon). We worry most how conservation managers may respond to these and other unsupported conclusions, such as which points in the supply chain and genera to target to reduce mislabeling. To focus on “…points in the chain-of custody beyond ports, where the majority of mislabeling occurred” is not supported by the data analyzed. Although more studies may have sampled at the retail level, the mislabeling detected could have happened at any point upstream of this level, including at ports. Likewise, how could managers select genera most prone to mislabeling when both labeled and true id genera are mixed in their analyses (figure 3)? If anything, this argues for tracing all seafood. Even if all their methods were robust and valid, which we argue are not, a less threatened substitute species sold as a marginally more threatened one does not remove the market DEMAND for the more threatened species; neither does it negate the need for accurate labeling for stock assessment purposes. The authors also did not adequately address how mislabeling impacts consumer perceptions of seafood sustainability or how mislabeling can facilitate illegal fishing. These erroneous conclusions may be used to support reduced regulatory focus on seafood mislabeling, ignoring the issue's very real complexities and conservation implications. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».