Informing Management of Atlantic Bluefin Tuna Using Telemetry Data
Bibliographic record
Abstract
Sustainable management of exploited marine fish and wildlife populations requires knowledge about their productivity. Survival from natural causes of mortality is a key component of population productivity, but is notoriously difficult to estimate. We evaluate the potential for acoustic telemetry as a long-term monitoring tool to estimate rates of natural mortality. We present a Bayesian multistate mark-recapture model for telemetry data collected over a decade from 188 Atlantic bluefin tuna (Thunnus thynnus)and apply it to estimate the rate of natural mortality using only acoustic tag detections for all animals, or using acoustic tag detections for 96 single-tagged tuna plus acoustic tag detections combined with estimated positions from pop-up satellite archival tags for 92 double-tagged animals. We support the model for bluefin tuna with a simulation study to quantify bias in estimates of population dynamics parameters and investigate the effect of auxiliary information from satellite tagging on mortality rate estimates for different acoustic tag detection probability scenarios. We obtained posterior estimates of the instantaneous annual natural mortality (survival) rate across a decade of tagging for Atlantic bluefin tuna of 0.17 yr-1(0.84 yr-1) both using only acoustic tagging data, and using a combination of acoustic and satellite tagging data. Use of a prior implying a higher rate of fishing mortality yielded an instantaneous annual natural mortality (survival) estimate of 0.10 yr-1(0.90 yr-1), with combined acoustic and satellite tag data. Results from the simulation study indicate that the use of satellite tags can improve the precision and accuracy of estimates of detection probabilities, area-specific movement probabilities and mortality rates, where the extent of the improvement depends on true underlying acoustic tag detection probabilities. Our work demonstrates that long-term acoustic tagging data sets have strong potential for monitoring of highly migratory marine fish and wildlife populations, providing information on a number of key parameters, including survival and movement rates. However, improved information on tag reporting rates or fishing mortality is needed to better separate natural and fisheries mortality for Atlantic bluefin tuna.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".