Disruptive viability selection on adult exploratory behaviour in eastern chipmunks
Bibliographic record
Abstract
Heterogeneous forces of selection associated with fluctuating environments are recognized as important factors involved in the maintenance of inter-individual phenotypic variance within populations. Consistent behavioural differences over time and across situations (e.g. personality) are increasingly cited as examples of individual variation observed within populations. However, the suggestion that heterogeneous selective pressures target different animal personalities remains largely untested in the wild. In this 5-year study, we investigated the dynamics of viability selection on a personality trait, exploration, in a population of eastern chipmunks (Tamias striatus) experiencing substantial seasonal variations in weather conditions and food availability associated with masting trees. Contrary to our expectations, we found no evidence of fluctuating selection on exploration. Instead, we found strong disruptive viability selection on adult exploration behaviour, independent of seasonal variations. Individuals with either low or high exploration scores were almost twice as likely to survive over a 6-month period compared with individuals with intermediate scores. We found no evidence of viability selection on juvenile exploration. Our results highlight that disruptive selection might play an important role in the maintenance of phenotypic variance of wild populations through its effect on different personality types across temporally varying environmental conditions.
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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) | 0.000 | 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".