Factors influencing the effectiveness of canada goose relocation in georgia, usa
Bibliographic record
Abstract
An iconic species of North American waterfowl, Canada geese (Branta canadensis) have established an overabundant resident population in Georgia, USA. As a consequence, wildlife managers respond to a growing number of complaints from landowners in urban areas where Canada geese pose a threat to property and human safety. Some landowners rely on relocation when other methods are unsuccessful at sites with nuisance Canada geese; however, some studies show that relocation may be ineffective when geese return to their original capture site. To analyze factors that may influence returning geese that were relocated in Georgia, I gathered data from USDA-APHIS on nuisance Canada geese that were captured, banded, and relocated to rural, hunted areas within Georgia from 2010 to 2019. I compared the nonrecaptured population with the recaptured population for differences in age, sex, and relocated distance. I found a relationship between age and recapture status (X2 (1, N = 4,058) = 14.17, p = .0002) as well as relocated distance and recapture status (X2 (2, N = 4,059) = 9.54, p = .0085), but no evidence of an association between sex and recapture status. There were fewer juvenile Canada geese than expected among the recaptured sample. In addition, among the recaptured sample, there were fewer than expected geese that were relocated greater than 250 kilometers away. I found an overall 2.5% recapture rate by USDA-APHIS personnel at nuisance sites. I recommend continuing relocation efforts at distances greater than 150 kilometers and at least 250 kilometers when possible. Due to nuisance complaints at recurring sites throughout the 10-year period, I recommend increasing initiatives to educate urban landowners in preventive and pre-planned measures (e.g., egg addling, predator decoys) to manage nuisance populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".