Variation in anti-predator behaviors of white-tailed deer (<i>Odocoileus virginianus</i>) in a multi-predator system
Bibliographic record
Abstract
Anti-predator responses to perceived predation risk can influence population demography. Understanding the relative effect of predator exposure and intraspecific interactions across a variety of anti-predator behaviors provides important insight into inter- and intra-specific drivers of species-specific behaviors. We merged classical behavioral observation methods with camera trapping techniques to examine anti-predator behaviors of white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) relative to variation in predator exposure and interspecific interactions. We coded 772 behavioral observations from >180 000 images spanning 605 trap-nights. Seasonal differences in behavioral responses to diminishing returns on foraging effort indicated that deer are risk-averse foragers and are more likely to exhibit risky behaviors in the presence of a concentrated food resource when the nutritional value of food resources are high. However, deer tended to spend less time at a site as resources diminished, supporting marginal value theories of habitat use. Intraspecific interaction predicted vigilance and the amount of time spent at a site. Similar to many other species, deer tended to be less vigilant and spend more time at a site as group size increased. Our research suggests that white-tailed deer in Wisconsin make foraging decisions based on factors associated with individual fitness, presence of conspecifics, and habitat characteristics.
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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 | 0.001 | 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) | 0.000 | 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 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".