Small scale fisheries in a warming ocean
Notice bibliographique
Résumé
Global warming, caused by the increase of greenhouse gas (GHG) emissions through human activities, has a strong impact on our oceans including changes to oceano-graphic characteristics, as well as to abundance and distribution of marine life. Moreover, it also has severe socio-economic impacts on people living at and from the sea. In order to predict and evaluate the impacts of global warming (and sub-sequently to find suitable adaptation strategies), scientific computer models are utilized. These climate change models predict the effects of global heating on marine life and associated fisheries on a global scale, but often with a high level of uncertainty and low geographic resolution. This makes it difficult to determine effective adaptation measures for fisheries on a local level. The development of adaptation and mitigation strategies is especially urgent in small-scale fisheries that contribute about half of global fish catches and make an important contribution to nutrition, food security, sustainable livelihoods and poverty alleviation, especially in developing countries. This study used a comprehensive conceptual framework that integrates different formats of knowledge, and an interdisciplinary research approach illustrated by the integration of both, the natural and the social sciences traditions. Our study aimed to explore local adaptation measures of fishers and fishing communities by complementing fine-grained scientific climate model predictions with insights based on the perceptions, knowledge, and practices local fishers have about climate change. This combined approach represents an innovative lens to understand climate change and human adaptation since it merges both predictive (computer models) and social sciences (traditional and local knowledge of fishers). We believe it will enhance our ability to promote and strengthen the natural capacity of adaptation of fishers and fishing communities with the aim to promote and support adaptation strategies of small-scale fishers. First, the modelling aimed to predict the climate change impacts on commercial fish species and their distribution in three case countries (Ecuador; mainland and Galapagos Islands, South Africa and the Philippines). These models were based on multitemporal data sets for the areas where the study took place, designed by using outputs of the IPCC scenarios and risk analysis methods. This allowed us to identify some of the anticipated impacts of climate change on the currently exploited fish stocks in those countries. The second part of the study aimed to i) explore local perceptions by fishers, of the effects of climate change on small-scale fisheries, ii) describe how well prepared the small-scale fishing sector is in front of climate change, and iii) illustrate the adaptation measures, capabilities, challenges, and actions, carried on by fishers, to cope with climate change. We organized four workshops (in the same three case countries) involving varied and relevant sectors and actors, within the small-scale fisheries sector. The workshops were attended by fishers, researchers and managers and exhibited diverse formats, based on the location’s and fisheries sector characteristics.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».