Bycatch analyses from inshore lobster fisheries in LFAs 27, 31A, 31B, 33, 34, 35
Notice bibliographique
Résumé
In 2013, the Government of Canada released the Policy on Managing Bycatch as part of the Sustainable Fisheries Framework. This policy identified Canada’s need to systematically address bycatch in all fisheries and included the objective of accounting for total catch, including retained and non-retained bycatch. In 2018, standardized protocols were introduced to an at-sea data collection program in Lobster Fishing Areas (LFAs) 33, 34, and 35 with sampling by two groups: an industry association and at-sea observer companies. Pre-existing voluntary industry-led programs in LFAs 27, 31A, 31B, and 32 were aligned with these protocols in 2018. The at-sea data collection program sought to sample a sufficient number of trap hauls to be representative of the entirety of the fishery. This program set a preliminary target to collect data from 1% of commercial fishing trips within each LFA in the inshore Lobster fishery. Combining all available years (2018–2021) and sampling sources (i.e., industry-led association and at-sea observer companies), more than 60,000 traps were sampled for bycatch with a total of 46 species or species groupings. These efforts represent between 0.09% and 0.59% of total commercial fishing trips and between 0.03% and 0.4% of total commercial trap hauls. Although these sampling targets were not met, analyses suggested sampling was representative of the fishery in most LFAs. Species richness varied between LFAs, but also between sampling sources within the same LFA. Results indicate the diversity of bycatch species in Lobster traps is well described in these sampling data. A generalized modelling framework that explicitly incorporates spatial-temporal dependence structure was applied to the at-sea sampled data to predict incidental capture of Atlantic Cod, Cusk, Jonah Crab, and Cunner. Sampling source and depth were evaluated as covariates in these models. The predicted incidental capture (bycatch) of Atlantic Cod in the Lobster fishery ranged from 1.02 tons (t)/fishing season in LFA 27 to 243 t/fishing season in LFA 34. Cusk were only present in at-sea samples from LFAs 33–35. The predicted incidental capture (bycatch) of Cusk in the Lobster fishery was 33.3 t/fishing season in LFA 33, 220.4 t/fishing season in LFA 34, and 1.8 t/fishing season in LFA 35. The predicted incidental capture (bycatch) of Jonah Crab ranged from less than 0.2 t/fishing season in LFAs 31A and 31B to 3,098 t/fishing season in LFA 34. While Cunner were present in all LFAs, bycatch analyses focused on LFA 27 where a Cunner retention pilot project is in place, and neighbouring LFAs 31A and 31B. The predicted incidental capture (bycatch) of Cunner was 9.8 t/fishing season in LFA 27, 1.05 t/fishing season in LFA 31A, and 0.57 t/fishing season in LFA 31B. To track annual changes in estimates of bycatch, increased spatial and temporal coverage of sampling would be required. Expanding the analyses to include data from sampling in western LFA 31B would improve spatial representativity of the LFA. The explicit incorporation of spatial and temporal effects should be considered in future investigations of bycatch.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».