Canada Goose Survival and Recovery Rates in Urban and Rural Areas of Iowa, USA
Bibliographic record
Abstract
ABSTRACT Once extirpated from much of their North American range, temperate‐breeding Canada geese ( Branta canadensis maxima ) have reached high abundance. As a result, focus has shifted from restoration to managing harvest and addressing human‐goose conflict. Conflict persists or is increasing in urban areas throughout the Mississippi Flyway. Managers need more information regarding demographic rates to determine how hunting affects geese breeding in urban areas and what management actions may be required to achieve management goals. We estimated survival, dead recovery, live recapture, and fidelity probabilities using data from 77,872 Canada geese banded in Iowa, USA, during 1999–2019 using Burnham joint live‐dead band recovery models. Factors predicted to affect parameters in candidate models included age (juvenile, subadult, adult), banding site (urban, rural), time, trend, harvest regulation index, and winter severity index. We predicted Canada geese banded in urban areas would have higher survival and lower dead recovery rates than geese banded at rural sites. The top model indicated support for age and banding site effects, and trends in survival and recovery rate (Brownie parameterization). Adult survival was similar for urban (0.75; range = 0.60–0.92) and rural (0.75; range = 0.66–0.82) geese and relatively constant across years. Mean juvenile survival was lower in urban (0.74; range = 0.48–0.93) than rural (0.85; range = 0.68–0.92) areas. Survival increased for urban‐banded juveniles and recovery rates increased during liberalization of harvest regulations and decreased after regulations stabilized. Recovery rates of subadults increased for the urban and rural groups. Our results suggest Canada geese breeding in urban areas contribute to harvest and specialized regulations can affect these populations. Harvest regulations in place during our analysis may not have reached a threshold required to observe substantial changes in survival. Current human‐goose conflict in urban areas suggests survival has not decreased to a level required to completely address conflict via reduction in goose abundance. Managers may consider additional liberalization of harvest regulations and monitoring via banding to determine to what degree hunter harvest contributes to reducing human‐goose conflict and what additional management actions will be required to achieve goals. © 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".