Isotopic fractionation and turnover in captive Garden Warblers (<i>Sylvia borin</i>): implications for delineating dietary and migratory associations in wild passerines
Bibliographic record
Abstract
There is currently a great deal of interest in using stable-isotope methods to investigate diet and migratory connections in wild passerines. To apply these methods successfully, it is important to understand how stable isotopes discriminate or change between diet and the tissue of interest and what the element-turnover rates are in metabolically active tissues. Of particular use are studies that sample birds non-destructively through the use of blood and feathers. We investigated patterns of isotopic discrimination between diet and blood and feathers of Garden Warblers (Sylvia borin) raised on an isotopically homogeneous diet (48% C, 5% N) and then switched to one of two experimental diets, mealworms (56.8% C, 8.3% N) and elderberries, Sambucus niger (47.4% C, 1.5% N). We established that the discrimination factors between diet and blood appropriate for stable carbon (δ13C) and nitrogen (δ15N) isotopes are +1.7‰ and +2.4‰, respectively. For feathers, these values were +2.7‰ and +4‰, respectively. Turnover of elemental nitrogen in whole blood was best approximated by an exponential-decay model with a half-life of 11.0 ± 0.8 days (mean ± SD). Corresponding turnover of carbon was estimated to range from 5.0 ± 0.7 to 5.7 ± 0.8 days. We conclude that this decoupling of nitrogen- and carbon-turnover rates can be explained by differences in metabolic routing of dietary macromolecules. Our results suggest that tracking frugivory in migratory passerines that switch diets between insects and fruits may be complicated if only a trophic-level estimate is made using δ15N measurements.
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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".