Cohort-Specific Differential Vulnerability to Capture of Mallards and Wood Ducks in Baited Swim-in Traps
Bibliographic record
Abstract
Abstract Attaching leg bands to birds directly before fall hunting seasons is a primary component of monitoring waterfowl in North America. Although capture and recovery data are primarily used for estimating survival and harvest distribution, these data may be used to estimate age ratios and other demographic rates, especially if recapture data are available from subsequent trapping and release events. We estimated recapture rates of mallards Anas platyrhynchos and wood ducks Aix sponsa and used those rates to estimate differential capture vulnerability, which is vital to estimate true age ratios from banding data. Posterior estimates of mallard age and sex cohorts and location-specific recapture rates varied among capture locations from a mean of 0.004 (0.002–0.008 95% credible interval) to 0.547 (0.486–0.609). Ratios of recapture rates among cohorts also varied, meaning no single differential vulnerability estimate would be useable across the study area. Our estimates of differential capture vulnerability for mallards, using the ratio of recapture probabilities, averaged 2.64 for adult female to adult male and 5.42 for juvenile female to adult female, with significant variation. Wood duck cohort-specific recapture rates were similar across locations. Similar wood duck recapture rates resulted in similar estimates of differential vulnerability, 1.24 for the ratio of adult female to adult male and 1.30 for juvenile female to adult female. The wide range of recapture rate estimates we found for mallards suggests that location-specific characteristics may have a strong effect on capture probability. Differences in recapture rates and apparent survival likely resulting from emigration suggest that if recapture data are to be used in population modeling, location-specific information is needed. The ability to monitor multiple demographic parameters using a single scheme improves continued assessment of population status. We recommend increased collection of in-season recapture data by biologists during active banding operations. Banders should be aware of the potential value of live, in-season encounters in monitoring populations and modeling demographic rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".