Coupling acoustic tracking with conventional tag returns to estimate mortality for a coastal flatfish with high rates of emigration
Bibliographic record
Abstract
The addition of acoustic telemetry to conventional tagging studies can generate direct estimates of mortality and movement rates to inform fisheries management. We applied a combined telemetry and tag-return design to southern flounder (Paralichthys lethostigma), a coastal flatfish that demonstrates limited movements within estuarine habitats coupled with extensive ontogenetic migrations that present unique challenges for estimating mortality rates. The fates of acoustically and conventionally tagged fish were followed during 2014–2016 to estimate annual rates of fishing mortality (F), natural mortality (M), and estuarine emigration (E). Multistate models estimated southern flounder annual F for each of the 3 years at two spatial scales (New River estuary F = 0.49–1.61; North Carolina coast F = 0.36–0.72). Annual rates of emigration were high (E = 1.06–1.67), and direct estimation of this source of loss considerably improved mortality estimates. The model estimated natural mortality as a constant annual rate (M = 0.84), which was similar in magnitude to life-history-based estimates for similar age groups. By accounting for unique behavioral attributes in the study design, the application of multistate tagging models provided robust estimates of mortality and emigration rates for a valuable coastal fishery resource that will inform future efforts to achieve yield and conservation goals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".