Total fishing mortality affecting Porbeagle shark in Atlantic Canadian waters
Bibliographic record
Abstract
Porbeagle Shark (Lamna nasus) are intercepted as bycatch from numerous fisheries in the Maritimes (MAR), Newfoundland and Labrador (NL), Gulf (GULF), and Quebec (QC) regions. From 2015 onwards, Porbeagle Sharks have been considered bycatch as there has been little incentive to land the species due to its low economic value. Given changes in fishing practices, these analyses considered data from 2015–2021. The three components of fishing mortality were considered: landings, at-vessel mortality (AVM), and post-release mortality (PRM) of discards. AVM represents animals that are dead upon gear retrieval, PRM represents animals that die following release. Most Porbeagle landings came from MAR, and landings were sporadic from all other regions. Within MAR, the majority was from benthic longline. However, landings make up a small component of total fishing mortality, as the majority of Porbeagle are discarded. Discard information was obtained from Canadian at-sea observer (ASO) data. Discards from observed trips represent a proportion of fishery-wide bycatch; therefore, they must be scaled up to represent fishery-wide values. Total discard weights from observed trips were highest in MAR, lower in NL, and lowest in GULF and QC. Fishery-wide estimates will be substantially higher due to low ASO coverage in numerous fisheries. Numerous statistical methods to estimate fishery-wide bycatch were identified and examined for ASO data from pelagic longline, given that this gear type was used historically to target Porbeagle. However, the quality and characteristics of the data precluded the use of these methods. For fisheries in MAR, total Porbeagle bycatch (kg) on observed trips was scaled up to fishery-wide estimates based on the proportion of trips that were observed. For fisheries in NL, total Porbeagle bycatch (kg) on observed trips was scaled up to fishery-wide estimates based on the proportion of target species landings that were observed. Bycatch weight on observed trips from fisheries in QC and GULF was not scaled up due to the lack of information on observer coverage levels and low levels of bycatch. There was information on AVM and PRM rates from few fisheries. Example scenarios applied different assumed AVM and PRM rates for fisheries in MAR and NL to estimate fishery-wide dead discards. The annual estimates varied by 6–20% among scenarios. Given demonstrated challenges and limitations of the available data, it is not possible to derive meaningful estimates of total annual fishing mortality of Porbeagle. Interpretation of the implications or impacts of observed increases or decreases in annual fishing mortality is not possible without information on underlying abundance and status of infrequently observed, discarded bycatch species (such as Porbeagle). This limits the utility of estimates of fishing mortality to address conservation or management goals, and warrants consideration of an alternate framework to quantify threats to bycatch species from fisheries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".