Neighbouring plants and perception of predation risk modulate winter browsing by white-tailed deer (<i>Odocoileus</i><i> virginianus</i>)
Bibliographic record
Abstract
The presence of neighbouring plants and predation risk can affect trophic interactions between plants and herbivores. We hypothesized that the relative preference for neighbouring species would determine winter herbivory and that predation risk would modulate browsing pressure. We tested these hypotheses using feeding trials in two regions with high white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) densities: Outaouais and Anticosti Island (Quebec, Canada). In each, we selected a species of interest and compared browsing rates and time spent foraging with neighbours relatively preferred or avoided. In a subexperiment, we included coyote (Canis latrans Say, 1823) urine to test for threat-sensitive foraging and interactions with neighbour effects. In Outaouais, time spent foraging on the focal species was reduced by the presence of potentially avoided neighbours and deer reduced browsing with increased perceived predation risk. On Anticosti, browsing rates on the focal species increased with avoided neighbours, with no effect of the predator urine. Anticosti deer have been in a predator-free environment for more than 120 years, likely reducing antipredator behaviours. This study demonstrates both neighbour effects and threat-sensitive foraging, phenomena that could interact and thus would benefit from being studied together to better represent trophic interactions in natural environments.
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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".