Updating the AIHTS Trapping Standards to Improve Animal Welfare and Capture Efficiency and Selectivity
Bibliographic record
Abstract
In 1999, after pressure from the European Union, an Agreement on International Humane Trapping Standards (AIHTS) that would result in the banning of the steel-jawed leghold traps in the European Community, Canada, and Russia was signed. The United States implemented these standards through an Agreed Minute with the European Community. Over the last two decades, scientists have criticized the AIHTS for (1) omitting species that are commonly trapped; (2) threshold levels of trap acceptance that are not representative of state-of-the-art trap technology; (3) excluding popular traps which are commonly used by trappers although they are known to cause prolonged pain and stress to captured animals; (4) inadequate coverage of capture efficiency and species selectivity (i.e., number of captures of target and non-target species) performance. Concerns about the ability of standards and test procedures to ensure animal welfare, and about the implementation of standards, have also been voiced by wildlife biologists, managers, and conservation groups. In this review, we present a synopsis of current trapping standards and test procedures, and we compare the standards to a then contemporary 1985-1993 Canadian trap research and development program. On the basis of the above-noted concerns about AIHTS, and our experience as wildlife professionals involved in the capture of mammals, we formulated the following hypotheses: (1) the list of mammal species included in the AIHTS is incomplete; (2) the AIHTS have relatively low animal welfare performance thresholds of killing trap acceptance and do not reflect state-of-the-art trapping technology; (3) the AIHTS animal welfare indicators and injuries for restraining traps are insufficient; (4) the AIHTS testing procedures are neither thorough nor transparent; (5) the AIHTS protocols for the use of certified traps are inadequate; (6) the AIHTS procedures for the handling and dispatching of animals are nonexistent; (7) the AIHTS criteria to assess trap capture efficiency and species selectivity are inappropriate. We conclude that the AIHTS do not reflect state-of-the-art trapping technology, and assessment protocols need to be updated to include trap components and sets, animal handling and dispatching, and trap visit intervals. The list of traps and species included in the standards should be updated. Finally, the concepts of capture efficiency and trap selectivity should be developed and included in the standards. Based on our review, it is clear that mammal trapping standards need to be revisited to implement state-of-the-art trapping technology and improve capture efficiency and species selectivity. We believe that a committee of international professionals consisting of wildlife biologists and veterinarians with extensive experience in the capture of mammals and animal welfare could produce new standards within 1-2 years. We propose a series of measures to fund trap testing and implement new standards.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".