Mark my words: experts’ choice of marking methods used in capture-mark-recapture studies of small mammals
Bibliographic record
Abstract
Abstract Crucial to the success of studies based on capture-mark-recapture (CMR) designs is the retention (permanency) and recognition (readability) of marks to identify individuals. Several marking methods for small mammals (< 60 g) are available, but their efficacy and use is not well known. We implemented a targeted survey of experts to gather their experiences and opinions regarding marking small mammals. Respondents (n = 114) stated their beliefs, perceptions, and current and future use, of marking methods, as well as factors influencing their choices, based on Likert and rank order scale questions. We compared responses based on where researchers’ studies occurred, their level of experience, and their subfield of mammalogy. Most respondents (73%) had > 5 years experience marking small mammals, with 60% each marking > 1,000 individuals. Respondents believed that ear-tagging was most preferable in terms of efficiency, impact to affected animals (survival, pain), and personal ethics, whereas passive integrated transponder (PIT)-tagging was the most preferable with regard to retention and recognition, and toe-clipping with respect to cost. Most respondents plan to use ear-tagging (78%) or PIT-tagging (70%) in the future. PIT- and genetic-tagging are expected to increase, and toe-clipping to decline, in the future. The factors influencing which marking method respondents used were ranked—in order of decreasing preference—as impact, retention, recognition, cost, efficiency, and ethics. There were few differences in the mean response or consensus among respondents, regardless of their experience, location, or subfield. Most respondents (66%) agreed that additional studies on the performance and impact of various marking methods are needed to assess their costs and benefits for CMR-based studies. Ultimately, choice of marking method will depend on the species, research question, available resources, and local legislation and permitting. Our study, however, illustrates that collective insights by experienced mammalogists may aid individual researchers in deciding on study designs and protocols, particularly early career scientists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".