Assessment of Scotian Shelf Snow Crab in 2019
Notice bibliographique
Résumé
Landings in 2019 for North-Eastern Nova Scotia (N-ENS) and South-Eastern Nova Scotia (S-ENS) were 629 t and 6,632 t, respectively, representing a decrease of 15% (N-ENS) and an increase of 9% (S-ENS) relative to the previous year . Total Allowable Catches in 2019 were 631 t, 6,632 t, and 0 t in N-ENS, S-ENS, and 4X, respectively. Due to low commercial biomass levels, there was no allowable catch in 4X for the 2018–19 season. Non-standardized catch rates in 2019 were 87 kg/trap haul in N-ENS and 105 kg/trap haul in S-ENS—which relative to the previous year represents an increase of 40% (N-ENS) and a decrease of 9% (S-ENS). The capture of soft-shelled Snow Crab in N-ENS decreased to 5% from approximately 25% in 2018. In S-ENS, the relative occurrence of soft-shell Snow Crab was approximately 2%, consistent with 2018. Soft-shell discard rates in 4X are traditionally very low, due to season timing. Bycatch of non-target species is extremely low (< 0.4%) in all Crab Fishing Areas (CFAs). In both N-ENS and S-ENS, moderate internal recruitment to the fishery is expected for next year (and likely for 2–4 years) based on size-frequency histograms. Based on survey catches, CFA 4X shows limited potential for substantial internal recruitment to the fishery for the next 4–5 years. Movement is potentially an important source of 4X Snow Crab. In all CFAs, there was a substantial recruitment of females into the mature segment of the population from 2016–2018. Mature Snow Crab densities are now declining but small Snow Crab (< 40mm carapace width) resulting from this period of increased egg production are now observed in all areas in both sexes. These population characteristics are tempered by a number of uncertainties, including the influence of predation and rapid temperature swings (especially in CFA 4X and parts of CFA 24). Both can have direct and indirect influences upon Snow Crab, which are cold-water stenotherms. Predation from halibut is a potentially large and increasing source of natural mortality for Snow Crab on the Scotian Shelf. A new peer-reviewed assessment methodology, conditional auto-regressive spatio-temporal model (carstm), has been adopted that incorporates both survey catches and ecosystem covariates to estimate a commercial Snow Crab abundance index. This index is coupled with a population-dynamics fishery model to determine fishable biomass. The modelled post-fishery fishable biomass index of Snow Crab in N-ENS was estimated to be 4,460 t, relative to 3,299 t in 2018. In S-ENS, the post-fishery fishable biomass index was 54,408 t, relative to 44,705 t in 2018. In 4X, the pre-fishery fishable biomass was 418 t, relative to 428 t in 2018. The N-ENS fishing mortality (F) in 2019 has been estimated to have been 0.14 (exploitation rate 0.13), a decrease from 0.22 in 2018. The S-ENS fishing mortality (F) in 2019 has been estimated to have been 0.12 (exploitation rate 0.13), a decrease from .13 in 2018.The F for 4X in 2018–2019 was 0 as there was no commercial fishery. With expected increasing recruitment for both N- and S-ENS, coupled with falling F over recent years, possibilities for harvest strategy is less limited. Additional work is required to determine more applicable (than current survey-based) Harvest Control Rules and associated management measures in 4X.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».