Effects of predation on telemetry-based survival estimates: insights from a study on endangered Atlantic salmon smolts
Bibliographic record
Abstract
Telemetry is increasingly being used to estimate population-level survival rates. However, these estimates may be affected by the detectability of telemetry tags and are reliant on the assumption that telemetry data represent the movements of the tagged fish. Predation on tagged fish has the potential to bias survival estimates, and unlike the issue of detectability, methods to correct for the resulting bias (termed “predation bias”) are not yet developed. In an acoustic telemetry study on inner Bay of Fundy Atlantic salmon (Salmo salar) smolts during 2008 and 2011, unusual tag detection patterns were indicative that some data may have been representative of the movements of predators rather than smolts. To incorporate predation effects into the resulting survival estimates, a suite of 11 summary migration metrics were compared between Atlantic salmon smolts and striped bass (Morone saxatilis). Cluster analyses revealed that 2.4% to 13.6% of tags implanted in smolts exhibited migration patterns more similar to striped bass than to other smolts, which was interpreted here as evidence of predation. Reassigning the fate of these tags as “depredated–died” reduced estimated survival from 43.5% to 41.1% in 2008 and from 32.6% to 19.0% in 2011 relative to a traditional mark–recapture model, illustrating the effect of predation bias in this case study.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| 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".