Using local dispersal data to reduce bias in annual apparent survival and mate fidelity
Bibliographic record
Abstract
In mark–recapture studies conducted on fixed-area study sites, apparent (or “local”) survival (ϕ) is the product of the probabilities of true survival (S) and site fidelity to the sampling area (F). If marked individuals permanently emigrate from the study site, apparent survival will be biased low relative to true survival. Similarly, estimates of mate fidelity will be biased high because site fidelity is typically higher for individuals that reunite with their previous mates than among pairs that divorce. Here, we develop a method for calculating site fidelity that takes plot boundaries into account, based on a dispersal model estimated from local movements within a fixed study site. We use dispersal estimates to adjust apparent survival and mate fidelity for the effects of short-distance movements out of a fixed area. We demonstrate our method with a retrospective analysis of a published study of 2 species of sandpipers breeding sympatrically at a field site in western Alaska. Estimates of survival probability increased by 0.01–0.03 for males and 0.07–0.08 for females in both species. The larger adjustments for females were expected based on their longer local dispersal movements. Adjusted mate fidelity estimates were lower than the original estimates by 0.04–0.07. Use of local movement data to estimate site fidelity cannot account for permanent emigration due to long-distance movements and, if such movements occur, our adjusted estimates of ϕ remain a function of true survival and site fidelity. Nevertheless, our method can reduce bias in demographic parameters resulting from local dispersal movements, improving estimates of annual survival and mate fidelity for use in demographic models.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".