The heritability of migration behaviours in a wide-ranging ungulate
Bibliographic record
Abstract
Abstract Migration behaviour is thought to be declining globally in the face of rapid human-mediated environmental change. While many species exhibit individual plasticity in their migratory behaviour, not all species demonstrate the level of plasticity necessary to adjust to novel conditions. Selection on heritable behaviours might therefore play an important role in the maintenance of migratory phenotypes for some species. Using GPS and genomic data from 242 individuals (256 animal-years) in a pedigree-free quantitative genetic approach, we estimated heritability, repeatability, and sources of environmental variation for migration traits in migrating mule deer ( Odocoileus hemionus ). We also estimated heritability of body size to ensure validity of our results. Heritability estimates of body size traits were comparable to the current estimates for ungulate body size. We found low heritability for broad patterns of migration timing, distance, and duration, but high heritability for movement rate along the migratory route. Our findings suggest that wild mule deer populations have the potential to respond to selection pressure generated by human activity or global environmental changes through microevolutionary changes in migration behaviours. Significance Statement Migration behaviour is critical for the reproduction and survival of a wide variety of taxa, yet there have been global declines in migrations in the face of rapid human-mediated environmental change. Despite our understanding that variation in migration behaviour has both genetic and environmental components, studies quantifying the sources of genetic variation contributing to migration phenotypes are lacking. Our study provides, to our knowledge, the first empirical evidence of heritability in a migration behaviour in ungulates. These results have implications for the evolution and maintenance of migration behaviours in natural 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 | 0.001 | 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 | 0.000 | 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".