Coping with the worst of both worlds: Phenotypic adjustments for cold acclimatization benefit northward migration and arrival in the cold in an Arctic‐breeding songbird
Bibliographic record
Abstract
Abstract Cold acclimatization (phenotypic adjustments to cope with cold conditions) is an imperative requirement for birds living at high latitudes during the cold depths of winter. Despite the significant remodelling of key phenotypic traits and energetic costs associated with elevating cold endurance, winter cold acclimatization can also provide further carryover benefits to subsequent stages in species wintering, migrating and breeding in cold environments (e.g. the Arctic). We tested this beneficial carryover hypothesis using outdoor captive Arctic‐breeding snow buntings Plectrophenax nivalis , a cold specialist known for its impressive wintering thermogenic capabilities. We compared changes in phenotypic traits supporting cold acclimatization—body composition (body, fat, lean mass, pectoral muscle thickness), oxygen carrying capacity (haematocrit), thermogenic capacity and endurance ( M sum , time to M sum ), cold tolerance ( T a at M sum ) and maintenance energy expenditure (BMR)—between the wintering, migratory and arrival/summer stages. Body mass (+31%), fat mass (+226%) and BMR (+13%) increased relative to the winter phenotype, likely to support the added costs of migration—that is the migratory upregulation hypothesis . In contrast, lean mass, pectoral muscle thickness, haematocrit and thermogenic capacity remained high and stable at winter level across stages in support of the thermal carryover hypothesis . The maintenance of these traits likely offers spare capacity for unpredictable cold environments expected during migration and breeding in the Arctic. Our results thus suggest that birds can extend the long‐term advantages of winter phenotypic adjustments through additional benefits to thermogenic capacity during subsequent life‐history stages. These benefits likely make it possible for Arctic‐breeding birds to maximize success across diverse life‐history stages in the face of extreme cold conditions.
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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".