Phenotypic constraints at the top of the world: an Arctic songbird faces the cumulative cost of maintaining a winter-like phenotype during breeding
Bibliographic record
Abstract
Among birds, several body composition traits typically decrease in size or mass during breeding likely as a result of competing demands during this critical life history stage. However, a recent outdoor captive study in an Arctic-breeding cold-specialist songbird (snow buntings – Plectrophenax nivalis) demonstrated that these birds maintain winter cold acclimatization during the spring and summer, despite facing summer temperatures much warmer than on their Arctic breeding grounds. This suggests that buntings may face a cumulative physiological cost during breeding: having to support a winter phenotype while also upregulating additional traits for reproduction. The current study aimed to test this hypothesis. Between 2016 and 2019, we examined how body composition and metabolic performance (thermogenic capacity and physiological maintenance costs) changed from pre-breeding to chick provisioning in free-living birds captured at the northern limit of their breeding range in the Canadian Arctic (Alert, NU, 82°). While body mass and fat reserves deceased significantly between pre-breeding and territory defense independent of thermal conditions, cold endurance and associated traits remained stable and elevated up to the nestling provisioning period, as long as ambient temperature remained below a threshold level of 0–2°C. These results indicate that snow buntings must maintain a high thermogenic capacity after arrival on the breeding grounds if temperatures remain below freezing, regardless of whether birds are actively breeding or not. In this context, our research suggests that these birds, and possibly other arctic breeding songbirds, may experience cumulative physiological costs during years with a late onset of spring, when breeding activities (i.e., egg production and incubation) begin while temperatures are still below 0–2°C.
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 | 0.001 | 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".