Mallards have been replacing Black Ducks in human-altered landscapes in Ontario, 1996-2019
Bibliographic record
Abstract
The American Black Duck (hereafter Black Duck) has received much attention over the years as a popular game bird species that experienced historical declines on its wintering grounds. Declines may be due to changes in the quality and quantity of breeding habitat, competition with Mallards, or both. Although the Black Duck population has stabilized, spatial variation in regional population trends of Black Ducks on their breeding grounds can provide insight into the relative importance of these alternative hypotheses at provincial and local scales. We analyzed indicated breeding pairs count data collected in Ontario between 1996 and 2019 through the Eastern Waterfowl Survey to evaluate the correlation in breeding densities and trends of the five most abundant waterfowl species. We also tested whether habitat and anthropogenic disturbance variables could explain the distribution of those species. Overall, Mallard breeding pair densities increased across the survey area, while Black Duck breeding pair densities decreased in the southern and northern part of the survey area but remained stable in the central part. Densities of breeding pairs of Black Ducks and Mallards were negatively correlated across survey plots but the trends in breeding densities were not correlated. Densities of Mallard breeding pairs increased in human altered landscapes whereas the density of Black Duck breeding pairs was negatively correlated with agriculture. Our results suggest that Mallards did not competitively exclude Black Ducks on the breeding grounds, but instead, Black Ducks were replaced by Mallards following encroachment of agriculture into Black Duck breeding habitat. Habitat management plans focused on breeding Black Ducks should emphasize reduction in the expansion of anthropogenic perturbations and increases in habitat conservation efforts in the central area of the breeding range in Ontario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".