Simulations of wood duck recruitment from nest boxes in Mississippi and Alabama
Bibliographic record
Abstract
ABSTRACT Since the early 20th century, wildlife managers have deployed artificial nesting structures for wood ducks (Aix sponsa) to increase availability of nest sites and local reproduction of the species. However, knowledge is lacking of the effects of nest structure size (i.e., large vs. small; Stephens et al. 1998) and reproductive data (e.g., clutch size, hatch date, duckling survival) on recruitment of wood ducks. We used stochastic simulation analyses to predict recruitment of wood ducks into late summer by analyzing data from a 6‐year study of box‐nesting wood ducks, and 4‐year (Mississippi) and 2‐year (Alabama) studies of radio‐marked female wood ducks and their ducklings. Our index of recruitment was the number of radio‐marked ducklings per nest box that survived until 1 September. Ducklings hatched after 1 June exhibited a 30‐day survival probability of 0.29, which was nearly 3 times greater than those hatched before 1 June. In east‐central Mississippi, 68% and 65% of total wood duck recruits from large and small boxes, respectively, were hatched and reared from June to August. In western Mississippi, 91% of recruits from each box size also were hatched and reared from June to August. Mean number of wood duck recruits produced from large boxes was greater than small boxes at each study site; each large box in western Mississippi produced approximately 4 recruits on average, whereas small boxes in east‐central Mississippi produced approximately 1 recruit. Wood duck recruits in our study resulted primarily from late spring and summer hatched birds in contrast to most Nearctic ducks with adaptive, early nesting to promote recruitment. In Mississippi and similar southern environments, we recommend use of large boxes and cleaning boxes around 1 May after completion of initial nests, and emphasize the importance of late spring and summer duckling production to wood duck recruitment. © 2015 The Wildlife Society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".