Social selection analysis reveals limited effect of neighbors’ traits in Tree swallows
Bibliographic record
Abstract
Social interactions are ubiquitous in nature and can shape the fitness of individuals through social selection. This type of selection arises when phenotypes of neighbors influence the fitness of a focal individual. Quantifying social selection is crucial to better characterize the overall selective landscape. For example, if intraspecific competition is strong, traits that are beneficial for an individual could be detrimental to competitors. In this study, we quantified social selection acting on three key ecological traits (body mass, wing length, and laying date) in wild Tree swallow (Tachycineta bicolor) females. We used reproductive success measured at three stages throughout the breeding season as fitness proxies to assess selection acting at those decisive moments. We also quantified the effects of environment on selection using measures of conspecifics' density, type of agricultural landscape, and presence of interspecific competitors. Overall, we found no strong evidence of social selection on these traits in our study system, although there were marginally nonsignificant selection gradients suggesting the positive effect of larger neighbors. Environmental variables affected reproductive success but did not strongly affect social selection gradients. Our study calls for more social selection estimates to be reported across environments to better understand its importance 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 | 0.000 | 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".