Does Water Temperature Affect the Timing and Duration of Remigial Moult in Sea Ducks? An Experimental Approach
Bibliographic record
Abstract
Aquatic birds have high cost of thermoregulation, especially during the moulting period, yet the effect of water temperature on the moulting strategy of aquatic birds has rarely been studied. Our general hypothesis is that energy savings associated with lower thermoregulation costs would be allocated to moulting processes. We predicted that aquatic birds moulting in warm water would have a higher level of body reserves, a faster growth rate of feathers, and an earlier remigial moult onset compared with birds moulting in cold water. We used the common eider (Somateria mollissima dresseri), a large sea duck, as the model species. Captive individuals were experimentally exposed to warm (18°C) and cold (8°C) water treatments during a three year period with individuals swapped between treatments. We found a similar feather growth rate for the two water temperature treatments and in contrast to our predictions, eiders exposed to warm water had a lower body mass and showed a delayed onset of remigial moult of approximately 7 days compared with those exposed to cold water. Our data indicate that body mass variations influence the timing of moult in unexpected ways and we suggest that it likely controls the occurrence of wing moult through a hormonal cascade. This study emphasizes the importance of improving our knowledge of the effects of water temperature on remigial moult of aquatic birds, to better assert the potential effects of global warming on their survival.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".