Data from: Environmental and demographic drivers of male mating success vary across sequential reproductive episodes in a polygynous breeder
Bibliographic record
Abstract
Ecological and social factors underpinning the inequality of male mating success in animal societies can be related to sex ratio, sexual conflict between breeders, effects of non-breeders, resource dispersion, climatic conditions, and the various sequential stages of mating competition that constitute the sexual selection process. Here, we conducted an individual-based study to investigate how local resource availability and demography interact with annual climate conditions to determine the degree of male mating inequality, and thus opportunity for sexual selection across two sequential reproductive episodes (harem and subsequent mate acquisition) in a naturally regulated (feral) horse population in Sable Island National Park Preserve, Canada. Using a 5-year, spatially explicit, mark-resight dataset and hierarchical mixed-effects linear modelling, we evaluated the influence of adult sex ratio (ASR) on mating success, and then tested for effects of freshwater availability, density, unpaired male abundance, and precipitation during each breeding season. Unpaired male abundance, freshwater availability, and ASR differed in their effects on male mating success according to year and selection episode. Opportunity for sexual selection in males associated with harem acquisition increased with ASR, and unpaired male abundance further explained weather-related inter-annual variation after accounting for ASR. In contrast, once a harem was secured, ASR had little effect on male mating inequality in regards to acquiring additional females, while inter-annual variation in mating inequality increased with decreasing freshwater availability. Our findings show that local demography, resource availability, and weather effect opportunity for sexual selection in males differently depending on selection episode, and can attenuate or accentuate effects of ASR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".