Elk and Deer Habituate to Stationary Deterrents in an Agricultural Landscape
Bibliographic record
Abstract
ABSTRACT Deterrents that are designed to emulate humans or natural predators are increasingly applied to manage the behaviors and distribution of conflict‐prone species, but the efficacy of these tools is frequently challenged by the process of habituation. In this study, we investigated the responses of Roosevelt elk ( Cervus canadensis roosevelti ) and black‐tailed deer ( Odocoileus hemionus columbianus ) to playbacks of unimodal (acoustic) and multimodal (acoustic and visual) stimuli on crop fields in the Cowichan Valley, British Columbia, Canada. We contrasted behavioral responses to acoustic recordings of (i) human voices (shouting and talking), (ii) natural predator vocalizations (wolf and cougar), (iii) dog barks, and (iv) local bird vocalizations (control) and tested the effect of flashing LED lights using alternating audio‐light and audio‐only treatments at the same sites over two 3‐week periods. We found that multimodal stimuli increased the likelihood of fleeing by 4.7 in elk and 1.8 in deer but did not affect the time spent in alert postures. Among acoustic treatments, playbacks of human shouts tended to elicit greater flight responses than humans talking, natural predators, dogs, and bird sounds. Both species showed evidence of habituation to the deterrents as the 6‐week experiment progressed, but elk responses declined more rapidly than deer, and rates of habituation for both species were slower when deterrents included flashing lights. For deer, alert responses declined more rapidly at sites surrounded by more houses and closer to highways. Together, our results indicate that recordings of humans shouting provided the most salient acoustic deterrent for these ungulates and that acoustic deterrents were enhanced with lights, but habituation to our stationary deterrents occurred rapidly, especially in proximity to human activity.
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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 | 0.000 |
| 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 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".