Climatic conditions cause spatially dynamic polygyny thresholds in a large mammal
Bibliographic record
Abstract
The polygyny threshold (PT) is a critical transition point in the sexual selection process for many organisms in natural populations, characterizing when females choose to mate with an already mated male over an unmated one to improve fitness. Understanding its causes and consequences is therefore of high interest. While both theoretical and empirical work suggest that the degree of polygyny within a species is plastic and a function of male inequality, the functional relationship between underlying availability of resources occupied by breeding males under variable climatic conditions and the dynamics of PTs across space and time has received less attention. Here, we use a standardized measure of male mating inequality as the culmination of female mate choices to analyse how spatially dynamic PTs in a naturally regulated feral horse (Equus ferus caballus) population emerge along a geographic gradient in a known, limiting resource (freshwater) each year from variable climatic conditions. Polygyny threshold distance from permanent freshwater increased with increasing precipitation during the breeding season of each year, suggesting a relationship between annual resource availability and female mate choice. The mechanism by which climatic conditions underpin the spatial dynamics of PTs was likely through precipitation providing ephemeral freshwater sources across the study area that effectively weakened the gradient in availability of permanent freshwater, thereby providing mating males that occupied home ranges far from permanent water with access to this limiting resource and enabling them to attract and retain females. Increased precipitation also coincided with a decreased proportion of males in the population that experienced sexual selection pressure attributed to female mate choice in relation to the acquisition and/or defence of freshwater sources. Climatic conditions caused spatial shifts in PTs annually along the geographic gradient in resource availability. Our findings reveal that such environmental gradients may either buffer or amplify impacts of climatic variation on selection pressure operating in natural populations, and emphasize the importance of integrating spatially explicit PTs with atmospheric fluctuations when predicting the effect of climatic change on selection processes within populations that occupy environmental gradients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".