Interspecific variation in antipredator behaviour leads to differential vulnerability of mule deer and white‐tailed deer fawns early in life
Bibliographic record
Abstract
Summary Ungulates are viewed as being highly susceptible to predation during the initial weeks or months of life. Yet aggressive defence by adult females is common in many ungulates and has the potential to reduce the vulnerability of the young significantly. We observed naturally occurring predatory encounters between coyotesCanis latransSay and deer fawns to test the hypothesis that a difference in aggressive defence leads to the differential vulnerability of mule deerOdocoileus hemionusRafinesque and white‐tailed deerO. virginianusZimmermann fawns in summer, when fawns are 0–14 weeks in age. Whitetail fawns suffer higher levels of coyote predation than do mule deer fawns at that time. The two species of deer are similar in size, but are known to differ in their antipredator behaviour in winter when fawns are older. Coyotes were less likely to attack mule deer than whitetail fawns they encountered, and were less likely to kill mule deer than whitetail fawns they attacked. The presence of a mule deer, but not a whitetail, female with a fawn deterred coyotes from attacking the fawn. Once attacked, fawns of both species were less likely to be killed when females defended them, but mule deer females were far more likely to defend fawns. Mule deer females defended fawns that were not their own offspring, including heterospecific fawns. Mule deer fawns were more likely to be defended if they had a larger number of females nearby when encountered. These observations raise the possibility that mule deer, and even whitetail, fawns may have improved survival in areas with higher densities of mule deer females. These results show that higher levels of defence by mule deer females reduced the vulnerability of mule deer fawns, contributing to the lower predation rates reported for mule deer than for whitetail fawns of this age group.
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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) | 0.001 | 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".