Distributions of threatened skates and commercial fisheries inform conservation hotspots
Bibliographic record
Abstract
Bycatch in commercial fisheries is a pressing conservation concern and has spurred global interest in adopting ecosystem-based management practices. To address such concerns, a thorough understanding of spatiotemporal relationships among bycatch species, their environment and fisheries is required. Here we used a generalized linear mixed model framework incorporating spatiotemporal random effects to model abundance patterns for 3 skate species caught as bycatch in commercial fisheries (thorny skate Amblyraja radiata, winter skate Leucoraja ocellata and smooth skate Malacoraja senta), as well as 10 target species on the Scotian Shelf, NW Atlantic. Spatiotemporal estimates of relative abundance for at-risk skates within the years 2005-2015 were modelled from research trawl survey data and overlaid with those for target species to identify hotspots of bycatch risk. In addition, abundance estimates for at-risk skates within the years 1975-1985, a period of higher stock abundance, were used to identify areas of previously important habitat. Historically, skate species densely occupied areas near Sable Island and Banquereau Banks, Georges Bank and the Bay of Fundy. Bycatch hotspots between at-risk skates and commercial targets were identified in regions across the Scotian Shelf. These hotspots were independently validated by predicting species presence from at-sea observer data that monitor skate bycatch directly. We discuss spatial relationships between target and bycatch species, highlighting limitations of at-sea observer programmes that this method helps to address. This framework can be applied more broadly to inform ecosystem management and priority areas for conservation or fisheries regulation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".