A multi‐isotope (δ<sup>2</sup>H, δ<sup>13</sup>C, δ<sup>15</sup>N) approach to establishing migratory connectivity of Barn Swallow (<i>Hirundo rustica</i>)
Bibliographic record
Abstract
Establishing geographic links between different stages of the annual cycle of migratory species is fundamental to evaluating factors limiting their populations. Stable isotope measurements (δ 13 C, δ 15 N, and δ 2 H) of feathers combined with a knowledge of how these isotopes are structured spatially in foodwebs can be used to establish molt origins and migratory connectivity. Here, we investigated patterns of migratory connectivity between North American breeding grounds and South American wintering grounds of stable ( n = 3) and declining ( n = 9) populations of Barn Swallow ( Hirundo rustica ) in North America using a probabilistic assignment to multi‐isotope feather clusters derived for 488 winter‐grown feathers collected during the breeding season (2009 to 2012). Our study did not find evidence for differential degree of migratory connectivity between increasing/stable and declining populations of Barn Swallows but found a longitudinal structure in breeding and wintering ground origins of populations. Probable wintering areas for northern and western breeding birds corresponded with western regions of South America, while birds breeding in southern and eastern North America tended to occupy areas in north‐eastern South America. Possible factors contributing to the differential population trends between stable or increasing and declining breeding populations could be related to habitat quality of the different wintering areas, changes in climate, and the cost of long‐distance migration. The use of a multi‐isotope approach and the combination of prior information on geographic distribution of vegetation types, based on δ 13 C measurements, effectively constrained geographic origins of swallows and our approach can be applied to defining migratory connectivity for other species.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".