Increases in the Number of American Black Ducks Wintering in Nova Scotia, 1970–2015
Bibliographic record
Abstract
Abstract Despite extensive conservation and management efforts, American black duck (Anas rubripes) populations remain below desired population levels. Researchers have noted declines at a number of wintering areas, but indications are that wintering populations in the northern part of the range are increasing. Intermittently between 1970 and 1989, and annually since 1992, wildlife biologists have flown aerial surveys of the entire coast of Nova Scotia, Canada, to count wintering waterfowl. This survey counted the total number of ducks seen in predefined lengths of coastline. We analyzed those survey data using generalized linear mixed models, over the entire time period (1970–2015) and in the recent time period (1992–2015, when surveys were done every year), across five general regions of Nova Scotia. We fit models using Bayesian methods with uninformative priors; models with negative binomial response distributions performed well. Due to the large variation in mean numbers of black ducks across the predefined lengths of coastline, we treated these blocks of coastline as a fixed effect, while annual trend (slope) was modeled as a random effect to account for variation in annual trend across blocks of coastline. Results were similar between the entire time series (1970–2015) or the most recent and more complete time series (1992–2015). In general, numbers of wintering black ducks in Nova Scotia increased (1.0–16.0%/y) and increases were significant (Bayesian credible intervals did not bound 0) in four of five regions. Increasing numbers in Nova Scotia are consistent with other observations of increasing wintering numbers at the northern range of American black ducks and may indicate a shift in the wintering range that has been ongoing for decades. Future habitat management actions will benefit from consideration of regional trends and the distributional change of black duck.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".