Kin-dependent dispersal influences relatedness and genetic structuring in a lek system
Bibliographic record
Abstract
Abstract Kin selection and dispersal play a critical role in the evolution of cooperative breeding systems. Limited dispersal dramatically increases relatedness in spatially structured populations (population viscosity), with the result that neighbours tend to be genealogical relatives. Yet the increase in neighbours’ performance through altruistic interaction may also result in habitat saturation and thus exacerbate local competition between kin. Our goal was to detect the footprint of kin selection and competition by examining the spatial structure of relatedness and by comparing non-effective and effective dispersal in a population of a lekking bird, Tetrao urogallus . For this purpose, we analysed capture–recapture and genetic data collected over a 6-year period on a spatially structured population of T. urogallus in France. Our findings revealed a strong spatial structure of relatedness in males. They also indicated that the population viscosity allowed male cooperation through two non-exclusive mechanisms. First, at their first lek attendance, males aggregate in a lek composed of relatives. Second, the distance corresponding to non-effective dispersal dramatically outweighed effective dispersal distance, which suggests that dispersers incur high post-settlement costs. These two mechanisms result in strong population genetic structuring in males. In females, our findings revealed a lower level of spatial structure of relatedness and genetic structure in respect to males. Additionally, non-effective dispersal and effective dispersal distances in females were highly similar, which suggests limited post-settlement costs. These results indicate that kin-dependent dispersal decisions and costs are factors driving the evolution of cooperative courtship and have a genetic footprint in wild populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".