Does reduced heterozygosity influence dispersal? A test using spatially structured populations in an alpine ungulate
Bibliographic record
Abstract
Despite having a profound effect on population dynamics, the reasons that animals disperse are poorly understood. Evolutionary explanations have focused on inbreeding and competition, where the potential cost of philopatry is negated through dispersal. Such scenarios lead to the prediction that less successful individuals preferentially disperse, termed 'fitness-associated dispersal'. Since heterozygosity is associated with fitness, we assessed whether dispersed animals had less observed heterozygosity (H(O)) than residents. We tested this prediction using both genetic and population-monitoring data of mountain goats (Oreamnos americanus). Individuals classified as dispersers through cross-assignment had the lowest mean H(O), followed by residents, and then admixed individuals. Dispersed individuals had 6.3 per cent less H(O) than their subpopulation of origin. In the long-term study of the mountain goat herd at Caw Ridge, Alberta, immigrants had the lowest H(O); however, the opposite pattern was seen in emigrants, which may be related to density dependence. This study is the first to provide empirical evidence that heterozygosity is associated with dispersal.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".