Assignment of tracks from tagged Atlantic bluefin tuna Thunnus thynnus to potential stocks using behavioural differences and habitat partitioning
Bibliographic record
Abstract
Introduction Atlantic bluefin tuna (ABT) are large, migratory pelagic predators of high economic importance. ABT are currently managed as two independent stocks assigned to discrete spawning areas (Gulf of Mexico, and Mediterranean); however, stock overlap outside spawning areas makes accurate assignment of catch to stock-of-origin difficult. Methods Within this two-stock paradigm, we characterised stock-specific spatial distributions and behaviours by comparing habitat usage and vertical movement behaviours of 118 electronically tagged adult ABT spatially assigned to the GOM and Med spawning grounds. These spatial and behavioural differences were used in tests to probabilistically assign unknown individuals (which did not visit the GOM/Med spawning areas) to a stock. Results This new methodological approach using existing tag data, enables increased assignment of a track to a potential stock, to be achieved before genetic assignments. We identified certain markedly different movement patterns, range extents, depth use preferences (and associated area usage), migration directness and speeds, corresponding distance from shore, and mesopelagic-layer visitation. The probabilistic assignment approach had 97% in-bag testing accuracy, then assigned 190 individuals to a stock, doubling the number of assigned stock-of-origin tracks in our dataset, and potentially revealing novel movement and behaviour patterns among pre-spawning-age ABT. Discussion This approach can be easily adapted to other study species, more stocks, and different testing variables, hopefully serving as a useful addition to the fisheries management toolkit.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".