Decoupling of body size, physiological state and subsequent performance in a developmentally manipulated songbird
Bibliographic record
Abstract
Abstract It is generally assumed that larger juveniles are more physiologically mature, hence their overall condition and subsequent performance are higher. Some taxa face extreme workload transitions during development (e.g. nest departure in birds), which may select for non‐linear growth profiles that decouple relationships between body size and physiological state, making the links between metrics of development (e.g. body mass) and juvenile performance uncertain. We manipulated perceived mass in European starlings approaching fledging using 4.0 g weighted backpacks, measuring subsequent growth trajectories (mass, wing length) and physiological state (aerobic capacity, energy state, oxidative status) to test whether body size and physiology are coupled during a non‐linear developmental phase (pre‐fledging mass recession). Nanotag radio transmitters were then used to track post‐fledging performance (activity, activity‐slope, dispersal). Weighted nestlings had greater pre‐fledging mass recession and marginally reduced wing growth when compared to controls. Consequently, weighted birds had lower body mass at fledging, while also displaying markers of oxidative stress (increased reactive oxygen metabolites), yet aerobic capacity and energy state were equal between treatment groups. Previously weighted birds showed no correlation between fledgling state (body size, physiology) and subsequent performance. However, control fledglings that had decreased mass recession and greater wing growth prior to fledging dispersed earlier, while longer wings at fledging were associated with more rapid increases in post‐fledging activity (activity‐slope). In addition, control fledglings with lower haemoglobin also dispersed earlier, while increased reactive oxygen metabolites were associated with decreased activity levels. Body mass and energy state were unrelated to any metric of post‐fledging performance. Our data suggest mass‐independent development of some physiological traits in species with non‐linear growth profiles, potentially with a context‐dependent oxidative cost, and that physiology may predict some measures of post‐fledging performance not captured by body size. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 |
| 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".