Site selection by geese in a suburban landscape
Bibliographic record
Abstract
Background In European and North American cities geese are among the most common and most visible large herbivores. As such, their presence and behaviour often conflict 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 population management; 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 hypothesise 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 examined landscape features that attract geese. These included the presence of islands in lakes, the distance from water, barriers to level flight and the size of exploited areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian goose) and the anserine geese, Branta canadensis (L. 1758) (Canada goose), Anser anser (L. 1758) (greylag goose) and Branta leucopsis (Bechstein, 1803) (barnacle goose). Landscape modification is a known method for altering goose behaviour, 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 islands, 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, islands and closely cropped lawns.
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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 | 0.000 | 0.000 |
| 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) | 0.005 | 0.002 |
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".