Warming in the land of the midnight sun: breeding birds may suffer greater heat stress at high- vs low-Arctic sites
Bibliographic record
Abstract
Abstract Rising global temperatures are expected to increase reproductive costs for wildlife as greater thermoregulatory demands interfere with essential breeding activities such as parental care. However, predicting the temperature threshold where reproductive performance is negatively impacted remains a significant hurdle. Using a novel thermoregulatory polygon approach, we predicted the threshold temperature at which an Arctic songbird–the snow bunting ( Plectrophenax nivalis )–would need to reduce activity and perform below the 4-times basal metabolic rate (BMR) required to sustain nestling provisioning to avoid overheating. We then compared this threshold to operative temperatures recorded at high (82°N) and low (64°N) Arctic sites to estimate how heat constraints translate into site-specific impacts on sustained activity level. We predict buntings would become behaviourally constrained at operative temperatures above 11.7°C, whereupon they must reduce provisioning rates to maintain thermal balance. Low Arctic sites had larger fluctuations in solar radiation, producing consistent daily periods when operative temperatures exceeded 11.7°C. However, high-latitude birds faced entire, consecutive days where parents would not be able to sustain required provisioning rates. These data indicate that Arctic warming is likely already disrupting the breeding performance of cold-specialist birds, but also suggests counterintuitive and severe negative impacts of warming at high-latitude breeding locations.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".