Application of stable isotopes to study movement ecology and diet variation in a migratory songbird
Bibliographic record
Abstract
Whether and how migratory organisms exhibit inter-individual behavioral and/or physiological variation across movement strategies remains an open question. The Dark-eyed Junco (Junco hyemalis) is a migratory songbird known for its intra-species variation displayed in relation to morphology, song repertoires, and migration. Thus, studies focusing on juncos can reveal how migratory strategy may covary with other individual-scale factors and, therefore, identify the selective forces driving intra-species variations throughout its distribution. We used Dark-eyed Junco hydrogen stable isotope feather values (delta2H) and implemented a Bayesian framework to infer the breeding and molting origin of migratory juncos captured on their winter grounds in Oklahoma, United States (U.S.). We modeled the distribution of feather hydrogen stable isotope values as a function of five morphological variables including body mass and fat deposition measured in Oklahoma during the winter. We then investigated the trade-off between longer and more energetically costly migration strategies in relation to diet preferences through carbon (delta13C) and nitrogen (delta15N) stable isotope analysis from feather values. Dark-eyed Juncos wintering in south central U.S. likely originate from multiple breeding populations in northern U.S. and Canada. Body condition at the wintering ground (e.g., mass) had no effect on feather hydrogen stable isotope abundance. However, we found a positive correlation between nitrogen and hydrogen stable isotopes, suggesting that a trophic level shift towards insect consumption might occur in individuals migrating from southern latitudes. Increased insect-derived protein consumption might be explained by reduced fatty-acid reserves necessary to complete a shorter migratory journey.
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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 | 0.001 |
| 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 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".