Long‐term effects of tagging fishes with electronic tracking devices
Bibliographic record
Abstract
Abstract Tagging fishes with internal or external electronic tracking devices (acoustic, radio, satellite, or archival tags) is invaluable to behavioural, ecological, and welfare research, but may have adverse effects on the animals studied. While short‐term responses to tagging (e.g., days to weeks) have often been investigated, less information is available on longer‐term impacts (e.g., months to years) and the potential chronic effects of tagging on basic biological needs such as foraging and reproduction. Here, we synthesize existing knowledge from peer‐reviewed acoustic, radio, satellite, and archival tagging articles ( n = 149) and anecdotal accounts ( n = 72) from 36 researchers to assess the effects of tagging over prolonged periods. We identified a dearth of research that has specifically measured or quantified the impacts of tagging over a period longer than a few weeks or months (e.g., median experimental study duration = 33 days; n = 120 articles). Nevertheless, there was limited evidence to support a net negative long‐term impact from the implantation or attachment of electronic devices. Considerations and future research directions are discussed with the goal of generating guidance to the research community and minimizing potentially detrimental impacts to study animals. Given the global application and relevance of electronic tagging research to inform conservation and management of fishes, it is imperative for scientists to continue evaluating how tagging procedures affect animal welfare, fate, and the interpretation of tracking data.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".