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Enregistrement W2974842986

GCFI versus global fisheries: marine ecosystems, food security and the data connection

2008· article· en· W2974842986 sur OpenAlexaffabout
Dirk Zeller

Notice bibliographique

RevueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2008
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFisheries and Aquaculture Studies
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésFood securityFisheryEcosystemConnection (principal bundle)Marine fisheriesMarine ecosystemEnvironmental scienceEnvironmental resource managementBusinessNatural resource economicsFishingEcologyEconomicsBiologyAgricultureMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Globally, the only source of fisheries catch data is the global landings database maintained by the FAO of the United Nations, which is mandated to rely on member countries' reported data.However, these data are known to be incomplete, as they generally focus on commercial and/or export fisheries, and report only poorly or not at all on all other fisheries sectors, including all non-commercial catches, such as subsistence or recreational fisheries, or illegal fisheries (Pauly and Zeller 2003).Yet, FAO data are used for regional and global status assessments.The Sea Around Us Project (Pauly 2007) has, as one of its projects, taken on the task of reconstructing total catches (in contrast to reported landings) per country.The first outcome of this was the realisation that China had over-reported its catches (Watson and Pauly 2001), which, once corrected for, given the magnitude of China's contribution to global catches, resulted in a clear demonstration that global landings had peaked in the late 1980s and appears to have been declining since.Subsequent work by us has shown that under-reporting of catches to FAO by most other countries, especially from small-scale fisheries, is so strong as to often invalidate inferences based on official figures (Zeller et al. 2006, Zeller et al. 2007, Zeller and Pauly 2007).This problem is even more dramatic in the arctic region spanning Canada, Alaska and the Siberian coast of Russia, from which, despite heavy reliance on hunting and fishing by the local population, zero catch is reported to FAO by these well developed countries (Booth and Watts 2007, Pauly and Swartz 2007, Booth and Zeller 2008).And the Caribbean region is not exempt from this problem (Baisre et al. 2003, Mendoza et al. 2003).Despite such fundamental data problems, fisheries resources play a crucial food security role in many countries, in particular so in developing countries (Jacquet 2009), including much of the Caribbean region.However, the current, 'developed country' style fisheries science and management, which traditionally has been 'exported' (generally with good intentions) to developing countries either implicitly as part of 'development projects', or indirectly through overseas training of local scientists and managers, is generally disconnected with respect to both ecosystem-based management needs and developing countries financial and human resource options.The solution lies in moving rapidly towards comprehensive approaches that do not rely on data-heavy methods first pioneered in data-rich and wealthy Europe and North America.This includes increased emphasis on spatial management, ocean zoning and the establishment and enforcement of no-take areas, which are now known to have strong potential to maintain and enhance long-term fisheries yield (despite some shortterm costs).Creating protected areas can also have the additional benefit of creating alternative livelihoods, such as those related to tourism.As far as individual countries and their governments are concerned, emphasis also has to shift away from a 'development' and 'commercial' profit oriented fisheries focus, to a more social and democratic focus that deals in the first instance with local, national and regional food security issues, well before profit margins.This has added urgency for developing countries, given the projected strong negative effects of climate change on local biodiversity and fisheries potential in the low latitudes (Cheung et al.In press-a, Cheung et al.In press-b).This can only be achieved by focusing on local small-scale fisheries (Pauly 2006), and being extremely cautious about any foreign access-based fishing in a country's EEZ waters, which have been shown to be only at the detriment and expense of the host country's resource security and livelihoods (Kaczynski and Fluharty 2002, Jacquet and Zeller 2007a, 2007b).

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,437
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,006
Études des sciences et des technologies0,0040,001
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,036
Tête enseignante GPT0,237
Écart entre enseignants0,201 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2008
Routes d'admission2
Résumé présentoui

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