Assessing Guinea Bissau's Legal and Illegal Unreported and Unregulated Fisheries and the Surveillance Efforts to Tackle Them
Notice bibliographique
Résumé
Fisheries in Guinea Bissau contribute greatly to the economy and food security of its people. Yet, as the ability of the country to monitor its fisheries is at most weak, and confronted with a heavy foreign fleet presence, the impact of industrial foreign fleets on fisheries catches is unaccounted for in the region. However, their footprint in terms of catch and value on the small-scale sector is heavily felt, through declining availability of fish. Fisheries in Guinea Bissau are operated by both legal (small-scale and industrial), and illegal (foreign unauthorized) fleets, whose catches are barely recorded. In this paper, we assess catches by both the legal and illegal sector, and the economic loss generated by illegal fisheries in the country, then attempt to evaluate the effectiveness of Monitoring Control and Surveillance (MCS) of Guinea Bissau’s fisheries. Two main sectors were identified through official reports and a literature review, the large-scale (industrial) sector, which between 2011 and 2017 included exclusively catches by foreign owned and flagged vessels, and catches by the small-scale sector, which remain largely unmonitored in official statistics. We use the available data on the number of legal and illegal vessels and/or fishers, and their respective catch per unit of effort to estimate catches, and we analyze monitoring outcomes against the registered industrial and artisanal fleets. We find that of the legal industrial vessels, 20% were linked to criminal activities in the past 7 years. These activities range widely from using an illegal mesh size, to fishing in a prohibited area, to labor abuse and drug trafficking. Overall, total small-scale and industrial catches were estimated at 370,000 t/year in 2017, of which less than 2% is ever reported to the FAO. Small-scale catches represented 8% of the total catch, and this contribution was found to be declining. Industrial fisheries generate over $458 million US, or which $75 million US is taken illegally, falling under the category trans-national fisheries crimes. The slight negative relationship between the number of monitoring days at sea illegal catches suggests increasing MCS efforts may play an important role in reducing illegal fishing in the country.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| 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 ».