Estimating total mortality among fisheries affecting Porbeagle shark (Lamna nasus) in Atlantic Canadian waters
Bibliographic record
Abstract
Porbeagle Sharks (Lamna nasus) in the northwest Atlantic are currently being considered for listing under the Canadian Species at Risk Act (SARA), and Fisheries and Oceans Canada (DFO) Science was asked to estimate total annual fishing mortality. This would come from landings, and at-vessel mortality (AVM) or post-release mortality (PRM) of discards from fisheries in the Maritimes (MAR), Gulf (GULF), Quebec (QC) and Newfoundland and Labrador (NL) Regions. This evaluation considers 2015 to 2021, representing a time period following the closure of the commercial Porbeagle fishery. Total landings remained low from Atlantic Canadian fisheries, dropping from 3.8 mt in 2015 to less than 200 kg in 2021. The vast majority of landings in 2015–2021 came from longline gear in MAR, primarily benthic longline in the Atlantic Halibut fishery with lower amounts from the pelagic longline fishery for Swordfish and Other Tunas. At-sea observer (ASO) coverage was variable among different fisheries and could not be estimated for fisheries in NL, GULF or QC. When coverage was low (< 5% annually), fisheries observed discards of Porbeagle substantially underestimated fishery-wide totals and needed to be scaled up to annual discard estimates. Also, several fisheries that would be expected to interact with Porbeagle had no ASO coverage and thus could not be considered in this assessment. A suite of statistical estimators was evaluated to model fishery-wide discards for pelagic longline in MAR. However, the data were not sufficient for quantitative models and thus these approaches were not applied. Simple scalars of either the proportion of observed trips (MAR) or proportion of observed target catch (NL) were used to approximate annual fishery wide bycatch from individual fisheries. Although estimates of total mortality were derived from scenarios assuming different AVM and PRM rates for various fisheries, they lacked precision and were predicated on numerous assumptions. Also, there were several factors that would have caused annual mortality to be underestimated but could not be corrected in advance of this assessment, so the magnitude of underestimation was unknown. Given demonstrated challenges and limitations of the available data, it is not possible to derive meaningful estimates of total annual fishing mortality of Porbeagle throughout Atlantic Canadian waters. Interpretation of the implications 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".