Mismatch between calf paternity and observed copulations between male and female reindeer: Multiple mating in a polygynous ungulate?
Bibliographic record
Abstract
, mate choice can be difficult to disentangle from male intrasexual competition because male behavior may constrain female choice. Multiple mating may provide an avenue for female mate choice, though it is difficult to identify using behavioral estimators alone. Molecular techniques address this issue by affording ecologists an opportunity to reassess mating systems from a genetic perspective. We assessed the frequency and possible explanations for multiple mating in reindeer using a genetic approach to determine the success of observed copulations in a semi-domesticated herd in Kaamanen, Finland. Behavioral and genetic data were synthesized with population characteristics over a 7-year period to test the hypothesis that, if present, polyandry in reindeer is driven by sexual harassment from sub-dominant males. We observed multiple mating in 42% of females, with as many as 60% exhibiting multiple mating in certain years. We found no evidence that multiple mating resulted from sexual harassment by sub-dominant males, suggesting that it is likely a deliberate strategy among females. Conversion rate of copulations into paternities varied with male size, with smaller males more likely to experience mismatch than larger males. Female preference for larger males persisted despite the occurrence of multiple mating, possibly suggesting a mechanism for cryptic post-copulatory selection. We suggest further research to delineate the possible influence of cryptic post-copulatory selection and multiple mating to defend against infertility in exhausted males.
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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 | 0.001 |
| 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".