Flyway population delineation in Taiga Bean Geese <i>Anser fabalis fabalis</i> revealed by multi‐element feather stable isotope analysis
Bibliographic record
Abstract
Fundamental to effective management of migratory waterbird populations is an understanding of their flyway delineation. Taiga Bean Geese Anser fabalis fabalis wintering in NW Denmark, Scotland and England are considered to originate from northern and central Sweden, southern and central Norway (‘Western flyway’), those wintering in southern Sweden, NE and southern Denmark are considered to originate from northern Fennoscandia and western Russia (‘Central flyway’), and those wintering in eastern Germany and Poland (which show far less favourable conservation status) are thought to come from western Siberia (‘Eastern 1 flyway’), although evidence to demonstrate this has largely been lacking. Evidence for different natal and moult origins of Taiga Bean Geese was investigated using stable isotope analyses of feathers of four elements (δ2H, δ13C, δ15N and δ34S). There were significant differences in isotopic composition of feathers from Swedish (Central) and German (Eastern 1) wintering populations and those moulting in Sweden in late summer (Western), which validated the three proposed major management flyway units above. The strong continental gradient in the stable hydrogen isotope ratios in precipitation (δ2Hp) across the region was used to assign wintering birds geospatially to natal and moulting origin, indicating separate natal and moulting areas for German (n = 37, from western Siberia) and Swedish (n = 20, Fennoscandia and more western Russia) wintering birds. These results confirm the largely discrete nature of these three flyways and contribute significantly to our ability to deliver effective targeted and appropriate research, monitoring and management actions throughout the ranges of these flyways.
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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) | 0.000 | 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".