From fear to food: predation risk shapes deer behaviour, their resources and forest vegetation
Bibliographic record
Abstract
The “ecology of fear” posits that predation risk shapes the behaviour of large herbivores, their foraging patterns, their habitat selection and their consequent effect on forest ecology. To test some of these predictions we used the extensive empirical and experimental data on vegetation cover and composition, and on deer anti-predator behaviour, collected at study sites with different histories of hunting and natural predation in the Haida Gwaii archipelago and in nearby areas of coastal British Columbia (Canada). Because these deer also forage in the intertidal, a habitat hypothetically more exposed to risk, we also analysed how risk affected intertidal foraging by measuring the proportion of marine versus terrestrial stable isotopes in deer bone collagen. In the absence of risk, deer had a strong negative effect on understory vegetation cover and plant composition. In these populations deer had a remarkable tolerance to human presence (short flight initiation and travel distances when disturbed), a willingness to consume foreign bait or to investigate baited traps, and a propensity to be active at daytime. Where deer faced long-term hunting and natural predators, understories were denser and more diverse and resembled those of forests never exposed to deer. Severe deer culling in sites initially without risk dramatically increased the cover of understory vegetation, although different in composition from the one in forests with long-term presence of predators and hunting, or that never had deer. Deer born after culling exhibited longer flight initiation distances and travel distances when fleeing, a reluctance to consume foreign bait or to investigate baited traps, and increased night-time foraging. The translocation of unwary deer from a population without risk to the island where culls had partially restored the vegetation, showed that their unwary behaviour was not significantly modified in the presence of abundant and higher-quality forage. This contrasted with the wary behaviour observed in the local deer born after the culls. Finally, deer in populations exposed to risk from hunters and/or predators were less likely to forage in the intertidal, although this trend might be, to some extent, affected by resources in the understory. We interpreted our results as evidence that risk is key in shaping, and potentially selecting for, lasting behavioural contrasts between deer populations, contrasts intimately connected to deer effects on plant cover and diversity, ecological networks, and ecosystem complexity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".