Male size and alternative mating tactics in white-tailed deer and mule deer
Bibliographic record
Abstract
Abstract Within populations, individual males adopt different courtship tactics due to differences in their competitive ability, which may vary depending on the animal’s age and size. To test the hypothesis that mule deer (Odocoileus hemionus) and white-tailed deer (O. virginianus) males vary their courtship behavior based on their size, we conducted focal observations of 144 mule deer and 85 white-tailed males that varied in size, at a large grassland site in southern Alberta. The smallest mule deer males devoted more time to feeding, were less likely to engage in late-stage courtship than larger males and were less likely to move among female groups. Other males, including small white-tailed males, appeared to use a roving strategy to search for estrous females in different groups, which is consistent with recent research on male movements. Both medium and large males increased the time they spent in one-male groups, and specifically isolated pairs, as courtship advanced, presumably to reduce competition with other males. However, this trend was most pronounced for medium mule deer males, and for all size classes of white-tailed deer. In contrast, large mule deer males spent a similar proportion of time tending females in all group types. Our results identified potential size-dependent tactics for mule deer males. In contrast, white-tailed males of all sizes appeared to rely on a tactic of finding and tending estrous females in isolation from other males.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".