Snow Buntings Maintain Winter-Level Cold Endurance While Migrating to the High Arctic
Bibliographic record
Abstract
Arctic breeding songbirds migrate early in the spring and can face winter environments requiring cold endurance throughout their journey. One such species, the snow bunting ( Plectrophenax nivalis ), is known for its significant thermogenic capacity. Empirical studies suggest that buntings can indeed maintain winter cold acclimatization into the migratory and breeding phenotypes when kept captive on their wintering grounds. This capacity could be advantageous not only for migrating in a cold environment, but also for facing unpredictable Arctic weather on arrival and during preparation for breeding. However, migration also typically leads to declines in the sizes of several body components linked to metabolic performance. As such, buntings could also experience some loss of cold endurance as they migrate. Here, we aimed to determine whether free-living snow buntings maintain a cold acclimatized phenotype during spring migration. Using a multi-year dataset, we compared body composition (body mass, fat stores, and pectoralis muscle thickness), oxygen carrying capacity (hematocrit) and metabolic performance (thermogenic capacity – M sum and maintenance energy expenditure – BMR) of birds captured on their wintering grounds (January–February, Rimouski, QC, 48°N) and during pre-breeding (April–May) in the Arctic (Alert, NU, 82°). Our results show that body mass, fat stores and M sum were similar between the two stages, while hematocrit and pectoralis muscle thickness were lower in pre-breeding birds than in wintering individuals. These results suggest that although tissue degradation during migration may affect flight muscle size, buntings are able to maintain cold endurance (i.e., M sum ) up to their Arctic breeding grounds. However, BMR was higher during pre-breeding than during winter, suggesting higher maintenance costs in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".