Consequences of hunter harvest, winter weather, and increasing population size on survival of non‐migratory Canada geese in Connecticut
Bibliographic record
Abstract
ABSTRACT In the last few decades, non‐migratory populations of Canada geese (Branta canadensis) have become established in metropolitan areas throughout North America. We banded 1,845 Canada geese in New Haven County, Connecticut, and studied goose survival of geese from 1984 through 2001, a period when local goose numbers increased several fold. Males outnumbered females among adults but not among juveniles. The hunter‐recovery proportion (probability that a goose was harvested by a hunter and its band reported to the U.S. Banding Lab) was 0.17 for all banded geese and was higher for males (0.19) than females (0.15). We used the Seber band‐recovery model in Program MARK to estimate the annual recovery rate and annual survival rate. The annual recovery rate was 0.22 for all geese and varied by year. The annual survival rate was 0.72 for all geese; survival was higher for females than males and higher for juveniles than adults. Survival rates varied among years and decreased in years with higher winter temperatures or more geese observed during Audubon's Christmas Bird Count. During our study, special hunting seasons in Connecticut targeted non‐migratory geese. Despite this, we found survival rates to be at the high end of values reported elsewhere, and the number of geese killed by hunters in Connecticut did not influence survival. Our results suggest that it will be difficult for wildlife agencies to rely solely on hunting to reduce the size of non‐migratory goose populations. © 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 | 0.001 |
| 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 | 0.000 |
| 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".