Data from: Indirect genetic and environmental effects on behaviours, morphology, and life-history traits in a wild Eastern chipmunk population
Bibliographic record
Abstract
Additive genetic variance in a trait reflects its potential to respond to selection, which is key for adaptive evolution in the wild. Social interactions contribute to this genetic variation through indirect genetic effects —the effect of an individual’s genotype on the expression of a trait in a conspecific. However, our understanding of the evolutionary importance of indirect genetic effects in the wild and of their strength relative to direct genetic effects is limited. In this study, we assessed how indirect genetic effects contribute to genetic variation of behavioural, morphological, and life history traits in a wild Eastern chipmunk population. We also compared the contribution of direct and indirect genetic effects to traits evolvabilities and related these effects to selection strength across traits. We implemented a novel approach integrating the spatial structure of social interactions in quantitative genetic analyses, and supported the reliability of our results with power analyses. We found indirect genetic effects for trappability and relative fecundity, little direct genetic effects in all traits and a large role for direct and indirect permanent environmental effects. Our study highlights the potential evolutionary role of social permanent environmental effects in shaping phenotypes of conspecifics through adaptive phenotypic plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".