Flexible response to short‐term weather in a cold‐adapted songbird
Bibliographic record
Abstract
To improve survival during winter, temperate species use a variety of behavioural and physiological adaptations. Among songbirds, the maintenance of lipid reserves is a widely‐used strategy to cope with the severity of winter; however, little is known regarding how multiple synchronously acting environmental mechanisms work together to drive these effects. In a context where temperate winter conditions are becoming more variable, it is important to widen our understanding regarding the flexible adaptations that may allow wintering species to adjust to projected climate change. Using a long‐term dataset collected across multiple wintering populations (7 years; 8 locations), we analyzed the effects of daily variation in weather (e.g. temperature, snowfall) on the variation in energy reserves (i.e. fat stores) of wintering snow buntings Plectrophenax nivalis . Our results support the prediction that birds carry more reserves to increase the safety margin against starvation when conditions are energy‐demanding and access to food is unpredictable (i.e. colder, snowier conditions). Birds responded to daily changes in weather by increasing their reserves as conditions deteriorated, with maximal temperatures and snow depth being the most important predictors of fattening decisions. We also found that females consistently exhibited higher fat reserves than males relative to their body size, suggesting that differential physiological adaptations among sexes or social dominance may play an additional role in explaining variation in energy reserves across individuals in this species. Overall, our findings increase knowledge on phenotypic adjustments used by species wintering in temperate zones to match variation in their environment.
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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".