Data from: A comprehensive large-scale assessment of fisheries bycatch risk to threatened seabird populations
Bibliographic record
Abstract
1. Incidental mortality (bycatch) in fisheries remains the greatest threat to many large marine vertebrates and is a major barrier to fisheries sustainability. Robust assessments of bycatch risk are crucial for informing effective mitigation strategies, but are hampered by missing information on the distributions of key life-history stages (adult breeders and non-breeders, immatures and juveniles). 2. Using a uniquely comprehensive biologging dataset (1697 tracks, 790 individuals), we assessed spatial overlap of four threatened seabird populations from South Georgia, with longline and trawl fisheries in the Southern Ocean. We generated monthly population-level distributions, weighting each life-history stage according to population age-structure based on demographic models. Specifically, we determined where and when birds were at greatest potential bycatch risk, and from which fleets. 3. Overlap with both pelagic and demersal longline fisheries was highest for black-browed albatrosses, then white-chinned petrels, wandering and grey-headed albatrosses, whereas overlap with trawl fisheries was highest for white-chinned petrels. 4. Hotspots of fisheries overlap occurred in all major ocean basins, but particularly the south-east and south-west Atlantic Ocean (longline and trawl), and south-west Indian Ocean (pelagic longline). Overlap was greatest with pelagic longline fleets in May–September, when fishing effort south of 25°S is highest, and with demersal and trawl fisheries in January-June. Overlap scores were dominated by particular fleets: pelagic longline – Japan, Taiwan; demersal longline and trawl - Argentina, Namibia, Falklands, South Africa; demersal longline – Convention for Conservation of Antarctic Marine Living Resources (CCAMLR) waters, Chile, New Zealand. 5. Synthesis and applications: We provide a framework for calculating appropriately-weighted population-level distributions from biologging data, which we recommend for future bycatch risk assessments. Many regions of high overlap corresponded with high seabird bycatch rates recorded by on-board observers, indicating that our approach reliably mapped relative bycatch risk. Implementation of effective bycatch mitigation in these high-risk regions varies considerably. Although potential bycatch risk appears to have decreased since the early 2000s, albatross and petrel populations from South Georgia and elsewhere are still declining, emphasizing the need for much improved observer coverage and monitoring of compliance with bycatch regulations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| 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 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".