Re‐weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behaviour and body condition of bats
Bibliographic record
Abstract
Abstract Considerable advances and breakthroughs in wildlife tracking technology have occurred in recent years, allowing researchers to gain insights into the movements and behaviours of a broad range of animals. Considering the accessibility and increase in use of tracking devices in wildlife studies, it is important to better understand the effects on these on animals. Our endeavour revisits a guideline established in 1988, which proposes that bats may encounter body condition or health problems and alter their behaviour when carrying tags weighing more than 5% of their body mass. Through a systematic literature review, we conducted a meta‐analysis to identify the impacts of tags on bats, including 367 papers from 1976 to 2023 that discussed, mentioned, employed, or quantified tagging of bats. We noted that the proportion of studies exceeding the 5% rule has not changed in recent years. However, the impact of tags was quantified in few studies for behaviour (n = 7) and body condition (n = 10) of bats. We were unable to assess whether tags weighing less or more than 5% of the bat's body mass impacted bats, but our meta‐analysis did identify that tags, irrespective of mass, affect the behaviour and body condition of bats. Although the overall magnitude of measured effects of tags on bats was small, progress has been made to advance our understanding of tag mass on bats. Naturally, there is a bias in reporting of significant results, illustrating the need of reporting results when there is no apparent effect of tags on bats. Our findings highlight the need for rigorous reporting of behaviour and body condition data associated with tagging of animals and illustrate the importance for studies comparing how tracking devices of different dimensions and masses may impact bat species to ensure research meets rigorous ethical standards.
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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 | 0.153 | 0.432 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".