Warming in the land of the midnight sun: breeding birds may suffer greater heat stress at high- versus low-Arctic sites
Bibliographic record
Abstract
Rising global temperatures are expected to increase reproductive costs for wildlife as greater thermoregulatory demands interfere with reproductive activities. However, predicting the temperatures at which reproductive performance is negatively impacted remains a significant hurdle. Using a thermoregulatory polygon approach, we derived a reproductive threshold temperature for an Arctic songbird—the snow bunting ( Plectrophenax nivalis ). We defined this threshold as the temperature at which individuals must reduce activity to suboptimal levels (i.e. less than four-time basal metabolic rate) to sustain nestling provisioning and 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 avoid overheating. Low-Arctic sites had larger fluctuations in solar radiation, consistently producing daily periods when operative temperatures exceeded 11.7°C. However, high-latitude birds faced entire, consecutive days when parents would be unable to sustain required provisioning rates. These data indicate that Arctic warming is probably already disrupting the breeding performance of cold-specialist birds and suggests counterintuitive and severe negative impacts of warming at higher latitude breeding locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".