Population structure of breeding Harlequin Ducks and the influence of predation risk
Bibliographic record
Abstract
Landscape features can have an important influence on the characteristics of populations, often resulting in heterogeneity in demographic processes. Therefore, local measurements of population parameters may not reflect regional characteristics. We studied populations of Harlequin Ducks ( Histrionicus histrionicus L., 1758) breeding in 11 river canyons in northern Labrador in relation to biophysical habitat characteristics and abundance of avian predators. Density and stability of Harlequin Duck populations varied among river canyons and were positively related (mean of 4.7 survey years per river). Both density and stability were negatively related to densities of raptorial birds. Raptor density was related to availability of suitable cliff ledges for nesting. Comparison of rivers with stable, high-density Harlequin Duck populations and those with variable, low-density populations revealed no detectable differences in habitat or prey availability. In a high-density population, observed stability but positive projected growth suggested the system was at carrying capacity and a source of emigrants. In contrast, unstable, low-density populations approached local extinction in some years, while large increases in subsequent years were suggestive of immigration. These findings demonstrate that breeding aggregations in different river canyons could represent an important unit of demographic structure. The abundance of raptors appears to be an important factor influencing local characteristics of Harlequin Duck populations. We discuss the potential influence of local demographic differences on regional population dynamics and their importance for conservation management strategies for migratory 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".