Combining telemetry and fisheries data to quantify species overlap and evaluate bycatch mitigation strategies in an emergent Canadian Arctic fishery
Bibliographic record
Abstract
Greenland halibut Reinhardtius hippoglossoides are bottom-dwelling flatfish that support commercial and community fisheries in Baffin Bay, Canada. Recently, exploratory inshore summer fisheries have raised concerns surrounding the bycatch of Greenland sharks Somniosus microcephalus and Arctic skate Amblyraja hyperborea , which are susceptible to overfishing due to their conservative life history traits. To explore fisheries selectivity and opportunities for bycatch mitigation, we combined pop-up satellite archival tags (PSATs) and fisheries data to assess habitat overlap and catch trends across these 3 species. PSAT data showed variable inter-specific overlap, with Greenland sharks primarily inhabiting depths <1000 m (725 ± 193 m), Greenland halibut inhabiting a narrower depth range (1030 ± 113 m), and Arctic skates overlapping depths (950 ± 225 m) of both species. However, fisheries data suggested high inter-specific overlap at deepest depths, with peak catch-per-unit-effort (CPUE) of all species at depths 800-1000 m. A marked decline in Greenland shark CPUE was observed throughout the fishing season which was best explained by cumulative fishing pressure. Combined tagging and fisheries data suggest that targeting specific seasonal habitat will not decrease bycatch, and inshore summer longline fisheries should be evaluated in the context of potentially high elasmobranch mortality, with enforced bycatch handling practices and alternative mitigation measures (e.g. gear modification or reduced soak times) required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".