Site selection by geese in a suburban landscape
Bibliographic record
Abstract
Background In northern European and North American cities geese are one of the commonest and most visible large herbivores that inhabit the suburban environment . As such, their presence and behavior often conflicts with the desires of the human residents. Fouling, noise, aggression and health concerns are all cited as reasons that there are “too many”. Lethal control is often used for control, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. We hypothesis that the landscaping of suburban parks can be improved to decrease its attractiveness to geese and to reduce the opportunity for conflict between geese and humans. Methods. Using observations collected over five years from a botanic garden situated in suburban Belgium and data from the whole of Flanders in Belgium we examine landscape features that attract geese, including the presence of islands in lakes, the distance from water, barriers to level flight and the size of grazing areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian geese) and the anserine geese, Branta canadensis (L. 1758) (Canada geese), Anser anser (L. 1758) (greylag geese) and Branta leucopsis (Bechstein, 1803) (barnacle geese). Landscape modification is a known method for modifying geese behavior, but there is little information on the power of such methods with which to inform managers and planners. Results. Our results demonstrate that lakes with islands attract more than twice as many anserine geese, than lakes without island, but make little difference to Egyptian geese. Furthermore, flight barriers between grazing areas and lakes are an effective deterrent to geese using an area for feeding. Keeping grazing areas small and surrounded by trees reduces their attractiveness to geese. Conclusion. The results suggest that landscape design can be used successfully to reduce the number of geese and their conflict with humans. However, this approach has its limitations and would require humans to compromise on what they expect from their landscaped parks, such as open vistas, lakes and closely cropped lawns.
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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 | 0.000 |
| 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".