Long‐Term Survival and Harvest of Resident Canada Geese in Virginia
Bibliographic record
Abstract
ABSTRACT Resident populations of Canada geese (Branta canadensis) are of particular management interest throughout the eastern United States given increased human‐wildlife conflicts due to regional increases in the Atlantic Flyway Resident Population. Within Virginia, USA, growth rates of resident goose populations have been reduced through extended harvest seasons and increased bag limits. Our objective was to investigate spatiotemporal patterns in survival rates and harvest rates of resident geese in Virginia over the past 25 years. We estimated annual survival, recovery, and harvest rates using mark‐recapture data from 1990–2015 for individuals that were banded as resident birds during summer throughout the state. We tested for differences in annual survival probability and harvest rates of resident geese banded and recovered in 3 distinct goose hunt zones: the Atlantic, Southern James Bay, and Western hunt zones, each of which had different hunting regulations. We also tested for differences in survival and harvest rates between individuals banded in rural or urban sampling locations, and between age classes (i.e., after hatch‐year or hatch‐year). In general, survival rates of resident geese over the past 25 years in Virginia are declining. Differences in survival among the 3 goose hunt zones also suggests that current harvest management strategies have reduced survival rates of resident geese. Upon closer examination, we found differences in survival among zones, with resident geese in the Atlantic and Southern James Bay hunt zones showing more negative declines compared to resident geese in the Western zone. Resident geese banded in rural areas had higher survival than urban‐banded geese. We also investigated the effects of sampling effort on survival estimates and found no difference in survival estimates among groups when using 75%, 50%, 25%, or 5% of the data randomly sampled from the full data set, suggesting that banding efforts of resident geese could be reduced and continue to inform adaptive management strategies for these populations throughout Virginia. © 2020 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".