Identification of predation events in wild fish using novel acoustic transmitters
Bibliographic record
Abstract
Abstract Background Acoustic telemetry is a commonly used tool to gain knowledge about aquatic animal ecology through the study of their movements. In telemetry studies researchers must make inferences regarding the movements and the fates of tagged animals. Until recently, predation has been inferred in telemetry data using a variety of methods including abrupt changes in movement patterns or habitat use. An acoustic telemetry transmitter has been developed to detect predation events of tagged animals, and while they have performed well in controlled laboratory trials, literature regarding the application of these novel transmitters in field settings is limited. The objective of this research was to describe the detection data obtained from field studies using predation tags and propose methods to incorporate this information in decision-making about the fate of tagged animals. We implanted 60 yellow perch ( Perca flavescens ) with predation transmitters and evaluated their spatial use in a receiver array (34 ha) using a combination of centres of activity, roaming indices, and step length measures to examine detection data. Results Over 5 months, 19 apparent predation events were identified by the transmitters. Roaming indices and centres of activity revealed a variety of detection patterns, including instances of altered behaviour before and after predation that matched tag-indentified predation events, dropped tags post-predation, and detections that ceased post-predation indicating the predator might have left the array. Based on the observed patterns, probable predation was inferred for 15 of 19 triggered tags, with unclear fates for four fish. Conclusions Our study provided a framework to assess the fate of animals tagged with predation transmitters and demonstrate how these tags can contribute to telemetry studies. We showed how detections can be categorized using tag status to compare movement metrics among individuals, provided tools to explore space use surrounding predation events, and synthesized this information to inform uncertainty surrounding tag-identified predation events. Predation tags do not remove all uncertainty about the fate of tagged individuals, but combined with other metrics they increase the likelihood of identifying abnormal movements that could otherwise introduce biased detection histories into studies of small-sized fishes.
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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 | 0.001 |
| 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) | 0.000 | 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".