Anti‐Predator Strategies and Grouping Patterns in White‐Tailed Deer and Mule Deer
Bibliographic record
Abstract
White‐tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) are closely related species of similar size that differ in their anti‐predator behavior. White‐tails flee from coyotes (Canis latrans), whereas mule deer typically stand their ground and attack this predator. I used observations of coyotes hunting deer to identify: (i) changes in group structure made in response to coyotes; and (ii) the relationship between group structure and the risk of predation for each species. In response to coyotes, groups of mule deer merged with other groups and individuals bunched together. Predation attempts were more likely to escalate when groups split and individuals failed to bunch. Coyotes typically attacked mule deer that were in outlying positions, and these deer had to move to central positions to end attacks. Due to the high frequency of attacks on small groups as well as to the level of dilution of risk, individuals in small mule deer groups were at high risk of being attacked compared with those in larger groups. In contrast to mule deer, white‐tails made no consistent changes in group size or formation, and coyotes attacked individuals in central as well as in outlying positions. Variation in aspects of group cohesion was not related to the vulnerability of white‐tails, and there was no obvious difference in the risk of attack facing individuals in groups of different size. These results suggest that coyote predation selects for relatively large, cohesive groups in mule deer, apparently because this type of group improves their ability to deter coyotes. Coyote predation does not have similar effects on groups formed by white‐tails, which use flight rather than deterrence to avoid predation. The benefits of responding cohesively, occupying certain positions within groups, and forming groups of a certain size can vary widely depending on the anti‐predator strategies used by an animal.
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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 | 0.001 |
| 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".