Intermediate frequency of aversive conditioning best restores wariness in habituated elk (Cervus canadensis)
Bibliographic record
Abstract
In protected areas around the world, wildlife habituate to humans and human infrastructure, potentially resulting in human-wildlife conflict, and leading to trophic disruptions through excess herbivory and disconnection of predators from prey. For large species that threaten human safety, wildlife managers sometimes attempt to reverse habituation with aversive conditioning. This technique associates people as a conditioned stimulus with a negative, unconditioned stimulus, such as pain or fright, to increase wariness and prevent the need for lethal wildlife management. Resistance to aversive conditioning by some habituated individuals often results in more frequent conditioning events by managers, but there are few studies of conditioning frequency with which to evaluate the usefulness of this management response. We evaluated the effect of conditioning frequency on the wariness of elk (Cervus canadensis) by subjecting marked individuals to predator-resembling chases by people over a period of three months. In that time, animals were subjected to conditioning a total of 3, 4, 5, 6, 7, or 9 times which we analyzed as both an ordinal variable and a binary one divided into low (3-5) and high (6-9) conditioning frequencies. We measured wariness before, during, and after the conditioning period using flight response distances from an approaching researcher. During the conditioning period, overall wariness increased significantly for elk in both treatment groups, although the increase was significantly greater in individuals subjected to high conditioning frequencies. However in the post-conditioning period, wariness gains also declined most in the high-frequency group, equating to more rapid extinction of learned behaviour. Across all treatment frequencies, rapid changes in flight responses also characterized the individuals with the lowest wariness at the beginning of the study period, suggesting that individuals with greater behavioural flexibility are more likely to habituate to both people and their attempts to change wariness via aversive conditioning. Together, our results imply that aversive conditioning may be most effective at intermediate frequencies and that its utility might be further increased with proactive assessment of individual personalities in habituated wildlife.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".