Short-term effects of hunting on naïve black-tailed deer (<i>Odocoileus hemionus sitkensis</i>): behavioural response and consequences on vegetation growth
Bibliographic record
Abstract
Hunting is commonly used to reduce overabundant deer populations that are of socioeconomic and ecological concerns. Recently, it has been suggested that “hunting for fear” might help maximize nonconsumptive effects of hunting and their cascading impacts. We investigated how a predator-free black-tailed deer (Odocoileus hemionus sitkensis Merriam, 1898) population introduced to the Haida Gwaii archipelago (Canada) responded to short-term hunting for fear. We conducted a hunt that minimized killing and maximized scaring on an island where some deer were marked. Between control and hunted sites, we compared deer use of bait stations and of shoreline areas (i.e., an open and risky area that provided marine subsidies). We also compared the browsing pressure and growth of two grasses and two shrubs. Hunting for fear had no effect on the overall use of bait stations and shorelines. However, in the area exposed to hunting, unmarked deer, which were assumed to be less tolerant of human disturbance, avoided bait stations at the outset of hunting. We concluded that individuals perceive and respond to risk differently. Furthermore, two of the four plant species were less likely to be browsed and had better growth in the experimental area than in the control area, suggesting an indirect effect of hunting on plants mediated by deer behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".