Models of Production Rates in American Black Duck Populations
Bibliographic record
Abstract
Understanding the relationship between the annual reproductive success and changes in environment is important for appropriate waterfowl management. We developed predictive models of American black duck (Anas rubripes) production rates as a function of biotic (black duck and mallard [A. platyrhynchos] abundance) and abiotic factors (spring precipitation and temperature) across predefined breeding areas, from 1990 to 2001. We used male age ratios in the fall population, estimated from wing samples of harvested black ducks corrected for differential vulnerability via band-recoveries, as the index to annual reproduction. Information criteria suggested that a model containing predictors for density-dependence, competition with mallards, spring precipitation, and temperature and stratum-specific coefficients was the best model of black duck production rates. However, coefficients of this model were highly imprecise, leading to relatively poor predictive ability, possibly due to multicollinearity among predictors and the relatively short time span of analysis. We fit several models that included only black duck and mallard abundance as predictors; of these, models with constant slopes and stratum-specific intercepts performed best. Model-averaged parameter estimates supported inverse relationships between black duck and mallard abundance and age ratios, with stronger relative effects for black duck density-dependence. Both effects have implications for adaptive harvest management, in that harvest potential for black ducks may differ greatly depending on combinations of number of each species and the relative belief in alternative hypotheses about the impacts of mallards. Much variability in age ratios remained unexplained by our models, some possibly due to the lack of habitat explanatory variables but also apparently due to random factors. Model improvement could be achieved by incorporating recent developments in the modeling of random effects, especially via Markov Chain Monte Carlo methods. More research is also needed to incorporate recently acquired habitat predictors into predictive modeling for black ducks and other ducks breeding in eastern North America. These results provide critical input for models of adaptive harvest management, currently under consideration as an approach for developing an international (Canada–U.S.) harvest strategy for black ducks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".