Intraspecific variation in male mating strategies in an African ground squirrel (<i>Xerus inauris</i>)
Bibliographic record
Abstract
Abstract Male mating strategies respond to female availability such that variation in resources that affect spatial distribution can also alter cost–benefit tradeoffs within a population. In arid‐adapted species, rainfall alters reproduction, behavior, morphology, and population density such that populations differing in resource availability may also differ in successful reproductive strategies. Here, we compare two populations of Cape ground squirrels (Xerus inauris), a sub‐Saharan species with year‐round breeding and intense mating competition. Unlike most mammals where males resort to aggressive interactions over females, maleX. inaurisare tolerant of one another, relying instead on other nonaggressive pre‐ and postcopulatory strategies to determine reproductive success. Our findings suggest that differences in resource availability affect female distribution, which ultimately leads to intraspecific variation in male reproductive tactics and sexual morphology. Sperm competition, assessed by reproductive morphometrics, was more pronounced in our high resource site where females were distributed evenly across the landscape, whereas dominance seemed to be an important determinant of success in our low resource site where females were more aggregated. Both sites had similar mating intensities, and most males did not sire any offspring. However, our low resource site had a higher variance in fertilization success with fewer males siring multiple offspring compared with our high resource site where more individuals were successful. Our results lend support to resource models where variations in female spatial distribution attributed to environmental resources ultimately impact male reproductive behaviors and morphology.
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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".