Shorebirds' Seasonal Adjustments in Thermogenic Capacity Are Reflected by Changes in Body Mass: How Preprogrammed and Instantaneous Acclimation Work Together
Bibliographic record
Abstract
Phenotypic flexibility in shorebirds has been studied mainly in the context of adjustments to migration and to quality of food; little is known on how birds adjust their phenotype to harsh winter conditions. We showed earlier that red knot (Calidris canutus islandica) can acclimate to cold by elevating body mass. This goes together with larger pectoral muscles, i.e., greater shivering machinery, and thus, better thermogenic capacity. Here, we present results of a yearlong experiment with indoor captive knots to determine whether this strategy is part of their natural seasonal phenotypic cycle. We maintained birds under three thermal regimes: constant cold (5 °C), constant thermoneutrality (25 °C) and natural seasonal variation between these extremes (9-22 °C). Each month we measured variables related to the birds' endurance to cold and physiological maintenance [body mass, thickness of pectoral muscles, summit metabolic rate (M(sum)), food intake, gizzard size, basal metabolic rate (BMR)]. Birds from all treatments expressed synchronized and comparable variation in body mass in spite of thermal treatments, with a 17-18% increase between the warmest and coldest months of the year; which appeared regulated by an endogenous driver. In addition, birds living in the cold exhibited a 10% higher average body mass than did those maintained at thermoneutrality. Thickness of the pectoral muscle tracked changes in body mass in all treatments and likely contributed to greater capacity for shivering in heavier birds. Consequently, M(sum) was 13% higher in cold-acclimated birds compared to those experiencing no thermoregulation costs. However, our data also suggest that part of maximal heat production comes from nonshivering processes. Birds facing cold conditions ate up to 25% more food than did birds under thermoneutral conditions, yet did not develop larger gizzards. Seasonal variation in BMR followed changes in body mass, probably reflecting changes in mass of metabolically active tissues. Just as cold-exposed birds, red knots in the variable treatment increased body mass in winter, thereby improving cold endurance. During summer, however, they maintained a lower body mass and thermogenic capacity compared to cold-exposed birds, similar to individuals kept at thermoneutrality. We conclude that red knots acclimate to seasonal variations in ambient temperature by modulating body mass, combining a preprogrammed increase in mass during winter with a capacity for fine-tuning body mass and thermogenic capacity to temperature variations.
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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".