Eastern Scotian Shelf Northern Shrimp (Pandanus borealis) stock assessment framework : model, indicators, and reference point development
Notice bibliographique
Résumé
The Eastern Scotian Shelf (ESS) Northern Shrimp fishery has been ongoing since the early– 1980s, although its contemporary history began in 1991 with introduction of the Nordmøre grate that reduced bycatch and enabled fishery expansion. In support of the fishery, an ESS Northern Shrimp stock framework CSAS peer-review was held over two meetings: 1. model development was peer-reviewed at a first meeting held on October 29–31, 2024; and 2. new indicators for the traffic light approach (TLA) were examined in context of the new model, and options for a new limit reference point (LRP) based on modeled results, were peer-reviewed at a second meeting held on March 5–6, 2025. This research document describes development of a stock assessment model for the ESS Northern Shrimp fishery in Shrimp Fishing Areas (SFA) 13, 14, and 15, as no analytical models have previously been implemented in these areas. Aspects of species and stock’s biology, distribution, and stock structure are presented, along with a brief description of the history of stock assessment for the fishery, in order to contextualize new developments. Data sources include fishery-dependent data from logbooks, port samples and observer trips, and fishery independent data from the Fisheries and Oceans Canada (DFO)-Industry survey, which deploys both a main trawl and a specialized belly bag. Three models —simplified delay-difference (SDD), tow level model (TLM), and Spatially Explicit Assessment Model (SEAM) — are described, fit to available data, and results compared to each other. While the SDD was deemed inappropriate for the ESS Northern Shrimp stock, both the TLM and SEAM indicated that exploitation rates have been relatively consistent over time, although ESS Northern Shrimp productivity has declined consistently since 2005, with 2023 demonstrating a marked decline and departure from past productivity expectations. There is evidence that the fishery has impacted stock dynamics, as both models indicated that biomass tends to decrease with exploitation rates above 6%. Both SDD and SEAM indicated that the least biased 1-year projection method was the mean growth approach. While both models are adequate for assessing the ESS Northern Shrimp stock, SEAM outperformed TLM, especially in terms of recruitment. As such, it is recommended that SEAM be used to provide science advice for the ESS Northern Shrimp stock using the median growth approach for 1-year stock projections. In the terms of reference points and indicators, results showed that LRPs based on maximum sustainable yield (MSY) simulations are not currently appropriate for the stock. An LRP based on an historical proxy for the theoretical long-term equilibrium biomass, in the absence of fishing (𝐵𝐵0), is proposed instead. In addition, most of the legacy TLA indicators demonstrated clear utility in the context of yearly science advice, with only a few inadequate indicators (i.e., Age 2, Snow Crab, Cod Recruitment, Turbot Abundance) being replaced by new and more useful indicators (i.e., Shrimp Bycatch, Area Occupied, and Atlantic Cod, Turbot, and American Plaice). In conclusion, the SEAM model, LRP, and stock indicators outlined in this framework research document received consensus support from peer-reviewers and meeting participants for use moving forward to assess the ESS Northern Shrimp stock.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».