Data from: Siring success in kangaroos: size matters for those in the right place at the right time
Bibliographic record
Abstract
In polygynous species, male reproductive success is predicted to be monopolized by a few dominant males. This prediction is often not supported, suggesting that ecological and alternative mating tactics influence siring success. The spatiotemporal distribution of individuals and the number of males competing for each receptive female are often overlooked, because they are difficult to monitor in wild animals. We examined how spatial overlap of female-male pairs, the time spent by a male on the breeding site, number of competitors and morphological traits influence siring probability in eastern grey kangaroos (Macropus giganteus). We compared home range overlap for 12,208 dam-male pairs and 295 known dam-sire pairs to define local competitive groups and to estimate every male’s opportunity to sire the young of each female. We compared models considering morphological traits relative to the entire population or to local competitive groups. Including local competition improved model performance because it estimated the intensity of competition and compared each male’s morphological traits to those of its competitive group. Regardless of size, males can increase their probability to sire a young by increasing their mating opportunity relative to the mother. We underline the importance of considering spatial structure to assess the intensity of competition in species where males cannot equally access all females in a population. The estimation of mating opportunity and intensity of local competition improves our understanding of how morphological traits affect siring success when each mating event involves a different set of competing males, a characteristic of most wild species.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".