Estimating dispersal among numerous sites using capture–recapture data
Bibliographic record
Abstract
Dispersal affects processes as diverse as habitat selection, population growth, and gene flow. Inference about dispersal and its variation is thus crucial for assessing population and evolutionary dynamics. Two approaches are generally used to estimate dispersal in free-ranging animals. First, multisite capture-recapture models estimate movement rates among sites while accounting for survival and detection probabilities. This approach, however, is limited in the number of sites that can be considered. Second, diffusion models estimate movements within discrete habitat using a diffusion coefficient, resulting in a continuous processing of space. However, this approach has been rarely used because of its mathematical and implementation complexity. Here, we develop a multi-event capture-recapture approach that circumvents the issue of too many sites while being relatively simple to be implemented in existing software. Moreover, this new approach allows the quantifying of memory effects, whereby the decision of dispersing or not on a given year impacts the survival or dispersal likelihood of the following year. We illustrate our approach using a long-term data set on the breeding ecology of a declining passerine in southern Quebec, Canada, the Tree Swallow (Tachycineta bicolor).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".