Natal origins and timing of migration of two passerine species through the southern Alps: inferences from multiple stable isotopes (<i>δ</i><sup>2</sup>H,<i>δ</i><sup>13</sup>C,<i>δ</i><sup>15</sup>N,<i>δ</i><sup>34</sup>S) and ringing data
Bibliographic record
Abstract
Understanding spatial linkages between areas used by migratory animals during the annual cycle is fundamental to their conservation. Stable isotope measurements of animal tissues can be a valuable tool in understanding spatial connectivity and migration phenology of migratory wildlife. We inferred natal origins of two migratory passerines, European Pied FlycatcherFicedula hypoleucaand European RobinErithacus rubecula, captured during autumn migration in the Italian Alps, by combining featherδ2H (δ2Hf) and ring recovery data. We used a spatially explicit likelihood‐based method to assign individuals to a precipitationδ2H surface calibrated to represent featherδ2H, together with the directional probability of origin from ring recoveries. The highest probabilities of origin for most individuals of both species were in central and north‐eastern Europe. Seasonal trends inδ2Hf, which described the species’ migratory phenology through the Italian Alps, were correlated with featherδ13C,δ15N andδ34S values, indicating strong spatial discrimination related to continental patterns for these isotopes. We demonstrate how this combined information can define catchment areas and migratory connectivity of birds intercepted in the Alps. We highlight the importance of ringing data in defining directional priors to define Bayesian‐based probability surfaces using continentalδ2Hfisoscapes, and how such information can be used to inform estimates of migratory connectivity.
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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 | 0.001 | 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) | 0.000 | 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".