Mating Group Size and Stability in Reindeer <i>Rangifer tarandus</i>: The Effects of Male Characteristics, Sex Ratio and Male Age Structure
Bibliographic record
Abstract
Abstract In polygynous mating systems, males compete intensely for mates and may mate several females during a single reproductive season. Accordingly, factors influencing the ability of males to control a larger number of females during the breeding season can provide information on the processes underlying sexual selection. In ungulates, age, body mass and social rank are considered good predictors of the reproductive success of males, but how male age structure and sex ratio in the population influence mating group (MG) dynamics has received little empirical testing. Between 1996 and 2005, we manipulated male age‐ and sex structure and monitored MG dynamics in a reindeer (Rangifer tarandus) population. We investigated the influence of male characteristics, percentage of males and male age structure on MG size and stability. We found that males with higher social rank (that were also older and heavier) controlled larger MGs (therefore had greater mating opportunities) and had more stable MGs (corresponding to a higher ability to maintain and control females) than males of lower social rank. Moreover, MG size and MG stability decreased as the percentage of males in the population increased, most likely resulting from greater male–male competition and increased female movements. Male age structure did not influence MG stability. Given the positive relationship between mating success and MG size (and likely MG stability), frequent female movements and intense competition among males to control females seem to be the principal components of reindeer MGs dynamic.
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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 | 0.000 |
| 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".