Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels
Bibliographic record
Abstract
Abstract Organisms can affect one another’s phenotypes when they socially interact. Indirect genetic effects occur when an individual’s phenotype is affected by genes expressed in another individual. These heritable effects can enhance or reduce adaptive potential, thereby accelerating or reversing evolutionary change. Quantifying these social effects is therefore crucial for our understanding of evolution, yet estimates of indirect genetic effects in wild animals are limited to dyadic interactions. We estimated indirect phenotypic and genetic effects, and their covariance with direct effects, for the date of spring breeding in North American red squirrels ( Tamiasciurus hudsonicus ) living in an array of territories of varying spatial proximity. Additionally, we estimated variance parameters and the strength of selection at low and high population densities. Social effects of neighbours on the date of spring breeding were weak at low, but stronger at high population densities. Indirect phenotypic effects accounted for a larger amount of variation in the date of breeding than direct differences among-individuals, although the genetic component to these indirect effects was not statistically significant. Nevertheless, the estimated effect size was large enough to suggest that indirect genetic effects could alter evolutionary change, resulting in less change at high densities despite stronger selection. Despite the difficulty in estimating them precisely, indirect genetic effects have clear potential to alter evolutionary trajectories in any natural systems where organisms interact.
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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 | 0.000 | 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".