Environmental heterogeneity and population differences in blue tits personality traits
Bibliographic record
Abstract
Environmental heterogeneity can result in spatial variation in selection pressures that can produce local adaptations. The pace-of-life syndrome hypothesis predicts that habitat-specific selective pressures will favor the coevolution of personality, physiological, and life-history phenotypes. Few studies so far have compared these traits simultaneously across different ecological conditions. In this study, we compared 3 personality traits (handling aggression, exploration speed in a novel environment, and nest defense behavior) and 1 physiological trait (heart rate during manual restraint) across 3 Corsican blue tit (Cyanistes caeruleus) populations. These populations are located in contrasting habitats (evergreen vs. deciduous) and are situated in 2 different valleys 25 km apart. Birds from these populations are known to differ in life-history characteristics, with birds from the evergreen habitat displaying a slow pace-of-life, and birds from the deciduous habitat a comparatively faster pace-of-life. We expected personality to differ across populations, in line with the differences in pace-of-life documented for life-history traits. As expected, we found behavioral differences among populations. Despite considerable temporal variation, birds exhibited lower handling aggression in the evergreen populations. Exploration speed and male heart rate also differed across populations, although our results for exploration speed were more consistent with a phenotypic difference between the 2 valleys than between habitats. There were no clear differences in nest defense intensity among populations. Our study emphasizes the role of environmental heterogeneity in shaping population divergence in personality traits at a small spatial scale.
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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".