Question the Mark: A Review and Assessment of Bat Marking Practices
Bibliographic record
Abstract
ABSTRACT Background It is often necessary to mark bats through tagging or other means to obtain essential information on their demography, movements and behaviour. However, marks may have lethal or sublethal effects and hence may bias study results. Understanding the effects of marks on bats will allow researchers and managers to develop guidelines to minimise effects. Aims Our aim was to review the effects and efficacy of marking techniques used on bats. Our objectives were to (1) describe marks currently used in bat research to identify motivations for marking, trends in commonly used types of marks and trends in the reporting of efficacy and injury rates in the recent literature, and (2) synthesise the body of literature on effects and efficacies of marking. Methods We conducted a targeted literature review and a systematic literature review. In the targeted review, we examined all papers on bat marking published from 2013 to 2022 in three bat‐ or mammal‐focused journals to identify trends in bat marking over the past decade. The systematic review was a general review of papers that reported on the effects and efficacy of bat marking from the early 1900s to the present. Results Our targeted review found that researchers rarely report the effects of marks on bats and many papers fail to provide details of the marks and marking procedures. Our systematic review found that the effects of marks ranged from minor irritation and behavioural changes to potentially life‐threatening injuries, such as changes in body condition; fewer deleterious effects have been reported from newer marking procedures such as passive integrated transponder (PIT) tags. Conclusions Further research on marking effects is needed, as well as more thorough reporting in the literature of marks and their effects so that useful guidelines can be developed.
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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 | 0.039 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".