Long-duration wind tunnel flights reveal exponential declines in protein catabolism over time in short- and long-distance migratory warblers
Bibliographic record
Abstract
During migration, long-distance migratory songbirds may fly nonstop for days, whereas shorter-distance migrants complete flights of 6 to 10 h. Fat is the primary fuel source, but protein is also assumed to provide a low, consistent amount of energy for flight. However, little is known about how the use of these fuel sources differs among bird species and in response to flight duration. Current models predict that birds can fly until fat stores are exhausted, with little consideration of protein’s limits on flight range or duration. We captured two related migratory species—ultra long-distance blackpoll warblers ( Setophaga striata ) and short-distance yellow-rumped warblers ( Setophaga coronata )—during fall migration and flew them in a wind tunnel to examine differences in energy expenditure, overall fuel use, and fuel mixture. We measured fat and fat-free body mass before and after flight using quantitative magnetic resonance and calculated energy expenditure from body composition changes and doubly labeled water. Three blackpolls flew voluntarily for up to 28 h—the longest wind tunnel flight to date—and ended flights with substantial fat reserves but concave flight muscle, indicating that protein loss, rather than fat, may actually limit flight duration. Interestingly, while blackpolls had significantly lower mass-specific metabolic power in flight than that of yellow-rumped warblers and fuel use was remarkably similar in both species, with consistent fat use but exceptionally high rates of protein loss at the start of flight that declined exponentially over time. This suggests that protein may be a critical, dynamic, and often overlooked fuel for long-distance migratory birds.
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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 | 0.001 | 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) | 0.001 | 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".