Variability of Female Responses to Conspecific vs. Heterospecific Male Mating Calls in Polygynous Deer: An Open Door to Hybridization?
Bibliographic record
Abstract
Males of all polygynous deer species (Cervinae) give conspicuous calls during the reproductive season. The extreme interspecific diversity that characterizes these vocalizations suggests that they play a strong role in species discrimination. However, interbreeding between several species of Cervinae indicates permeable interspecific reproductive barriers. This study examines the contribution of vocal behavior to female species discrimination and mating preferences in two closely related polygynous deer species known to hybridize in the wild after introductions. Specifically, we investigate the reaction of estrous female red deer (Cervus elaphus) to playbacks of red deer vs. sika deer (Cervus nippon) male mating calls, with the prediction that females will prefer conspecific calls. While on average female red deer preferred male red deer roars, two out of twenty females spent more time in close proximity to the speaker broadcasting male sika deer moans. We suggest that this absence of strict vocal preference for species-specific mating calls may contribute to the permeability of pre-zygotic reproductive barriers observed between these species. Our results also highlight the importance of examining inter-individual variation when studying the role of female preferences in species discrimination and intraspecific mate selection.
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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 | 0.001 | 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".