Killer tags: estimating the effect of eavesdropping predators in acoustic tagging projects on fish
Bibliographic record
Abstract
Ultrasonic coded transmitters (UCTs) operating at frequencies inaudible to humans are widely used to study behavior and life history of fishes including some highly endangered stocks. Recent research has demonstrated that most marine mammals can detect the transmission signal and that some species will spontaneously learn to associate such signals with a food reward. Sensory data from pinnipeds suggest they are able to detect fish tagged with UCTs at functional distances of several tens of meters. Detection by odontocetes may well occur over several hundred meters. In addition to detection range, mortality inflicted by eavesdropping predators depends on the encounter-rate of individual predators with tagged fish, as well as the predator’s learning rate. We present the results of a meta-analysis to determine the number of studies using UCTs in situations where predator eavesdropping may be an issue and to determine the number of tagged fish. We also use agent-based models to estimate mortality under different predation and learning scenarios in two Canadian study systems: Gray seals predating on gadid fish on the Scotian Shelf, and killer whales feeding on salmonids in the coastal waters of British Columbia. These models use predator and prey movement data from the published literature to estimate encounter rates and compare these to exposure rates known to cause associative learning in empirical studies. The meta-analysis showed that, while globally at low densities, the use of UTCs in some areas has reached levels where predator eavesdropping could be a serious concern. The agent-based models suggest that some predators in both systems experience tagged fish at sufficiently high frequencies to facilitate a dinner-bell effect. While the results of these models must be seen as preliminary, they clearly indicate that further research is required to investigate tag-effects of UTCs caused by eavesdropping predators.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".