Individual Variation in Migration and Wintering Patterns of Long‐Tailed Ducks <i>Clangula hyemalis</i> From a Population in Decline
Bibliographic record
Abstract
has declined dramatically since the 1990s at the species' most important wintering area, the Baltic Sea. It is unclear if this represents a real population decline at the flyway level or merely a northward shift in the wintering range, with part of the population moving from the Baltic Sea to rarelysurveyed ice-free Arctic waters. To investigate wintering area choice and individual repeatability, we deployed light-level loggers on female long-tailed ducks at three breeding sites in the Western Russian Arctic across two annual cycles, from 2017 to 2019. We obtained data from 94 year-round migration tracks (78, 14 and 2 from each breeding site) from 65 females. Females moved from freshwater breeding sites to mostly marine post-breeding sites after wing moult. For wintering, the majority of the birds (94%) migrated to the Baltic Sea, while the rest overwintered in the White and Barents Seas. Spring migration involved staging at marine sites in the Arctic Ocean for most birds. Individual repeatability scores were high for longitudes of wintering sites, departure dates from breeding and wintering sites, and low for arrival dates at breeding and wintering sites. Therefore, our results suggest that the observed decline in the long-tailed duck wintering population in the Baltic Sea is unlikely the result of a shift in wintering range within individuals, so that a real decline in the population size remains the most parsimonious explanation. High repeatability values indicate that the substantial variation in wintering sites throughout the Baltic Sea is clearly attributable to between-individual variation rather than within-individual variation across years. Still, addressing the underlying causes of population decline remains a challenge for this Arctic-breeding 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 | 0.000 | 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) | 0.001 | 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".