Behavioral responses of Canada geese to winter harassment in the context of human‐wildlife conflicts
Bibliographic record
Abstract
Abstract Wildlife harassment (i.e., intentional disturbance by humans) is a common nonlethal management approach employed to reduce human‐wildlife conflicts, but effectiveness is often undocumented or uncertain. We evaluated the effect of harassment on Canada goose ( Branta canadensis ) behavior in an urban area during winter. Winter can be a challenging period for waterfowl given limited food availability and greater thermoregulatory costs; thus, we expected that harassment in winter may be more effective than during other portions of the year. We used GPS transmitters equipped with accelerometers to evaluate the effects of harassment, weather conditions, and breeding origin location on goose movements, land cover use, emigration, survival, and behavior. Harassment caused geese to leave the harassment site more often (3.5 times) than on days when not harassed, but geese returned quickly after harassment (1.9 times) than without harassment. Harassment of geese affected specific goose behaviors (foraging, resting, flying, and alert), but effects of harassment were relatively small compared to the effects of weather conditions. Changes in land cover use were impacted by weather conditions, independent of harassment. Our findings suggest that harassment was ineffective at significantly changing site use or behaviors of geese and repeated harassment had diminishing returns. Geese moved to specific land cover resources that serve as sanctuaries (e.g., open waterbodies) during periods of extreme cold to engage in energetically conservative behaviors (i.e., resting). Harassing geese in areas that provide sanctuary during extreme cold periods or the use of lethal management in coordination with targeted harassment may be more effective than harassment alone in urban areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".