Estimating Natural Mortality of Atlantic Bluefin Tuna Using Acoustic Telemetry
Bibliographic record
Abstract
Abstract Atlantic bluefin tuna (Thunnus thynnus) are highly migratory fish with a contemporary range spanning the North Atlantic Ocean. Bluefin tuna populations have undergone severe decline and the status of the fish within each population remains uncertain. Improved biological knowledge, particularly of natural mortality and rates of mixing of the western (GOM) and eastern (Mediterranean) populations, is key to resolving the current status of the Atlantic bluefin tuna. We evaluated the potential for acoustic tags to yield empirical estimates of mortality and migration rates for long-lived, highly migratory species such as Atlantic bluefin tuna. Bluefin tuna tagged in the Gulf of St. Lawrence (GSL) foraging ground (2009–2016) exhibited high detection rates post release, with 91% crossing receiver lines one year post tagging, 61% detected after year two at large, with detections up to ~1700 days post deployment. Acoustic detections per individual fish ranged from 3 to 4759 receptions. A spatially-structured Bayesian mark recapture model was applied to the acoustic detection data for Atlantic bluefin tuna electronically tagged in the GSL to estimate the rate of instantaneous annual natural mortality. We report a median estimate of 0.10 yr−1 for this experiment. Our results demonstrate that acoustic tags can provide vital fisheries independent estimates for life history parameters critical for improving stock assessment models.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".