AN INTEGRATED CAPTURE–RECAPTURE AND STABLE-ISOTOPE APPROACH TO MODELING SOURCES OF POPULATION RESCUE
Bibliographic record
Abstract
Populations occupying "sink" habitats can persist if productive local subpopulations exist, if immigrants rescue the population, or if both processes occur. We tested these hypotheses by combining capture–mark–recapture data and recruit origin assignments obtained from feather stable-isotope values from a local Mallard (Anas platyrhynchos) population breeding near Minnedosa, Manitoba, during 2002–2005. Effects important to population growth rates included female nest location (nest tunnel vs. non-tunnel) and recruitment of yearling females originating locally or from nearby parkland areas rather than from more distant regions. Population growth rates of tunnel-nesting females (mean = 1.21 ± 0.22 [SD], n = 3 years) annually exceeded population stability, primarily because the apparent annual survival rate was consistently >0.75. Population growth rates for non-tunnel females (0.90 ± 0.17, n = 3 years) varied annually in response to recruitment rates of yearling females from Aspen Parkland areas; there was little support for models that represented population rescue by yearling immigrants produced in the U.S. Prairie Pothole and Canadian Boreal Forest regions. Elevated apparent survival rates of adult females using nest-tunnel locations and fine-scale dispersal of yearling females recruited from local subpopulations or nearby source populations are responsible for sustaining local Mallard levels near Minnedosa. This is the first study to demonstrate clearly the value of nest tunnels for increasing adult female survival and productivity of Mallards at a local scale. Integrating mark–recapture and isotopic information may be valuable in testing ecological and management hypotheses about dispersal and demographic processes, as well as in determining the value of nest-structure programs for avian conservation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".