Costs and benefits of natal dispersal in yearling mallards <i>Anas platyrhynchos</i>
Bibliographic record
Abstract
Costs and benefits of natal dispersal have not been fully evaluated in birds. We compared timing of breeding and nesting success for yearling female mallards Anas platyrhynchos returning to or dispersing from their natal areas. Information about natal origins was discerned with feather‐isotopes and combined with detailed reproductive histories for 503 radio‐marked females monitored at 16 study sites across the Canadian Aspen Parklands, during 1993–2000. A natal origin assignment model based on feather‐ δ 34 S, δD, δ 15 N, and δ 13 C values correctly assigned 81% (112 of 138) of known‐source yearlings to region of origin; region‐specific rates ranged from 70–90%. Timing of breeding and nesting success was not related to whether or not a female had dispersed from its region of natal origin in Aspen Parkland (i.e. short‐distance dispersal) versus the southern prairies or boreal forest regions (i.e. long‐distance dispersal). Rather, nesting success was best modeled to include effects of site‐specific wetland and breeding pair abundances and an interaction between local breeding pair and wetland densities. Nest success performance relative to dispersal distance varied among study sites but was unrelated to local upland nest cover, wetland habitat conditions, or conspecific density. Thus, we detected no strong costs of dispersal but some evidence that long‐distance dispersers presumably benefitted when they were able to acquire better nest sites.
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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 | 0.000 | 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".