Plant traits associated with seed dispersal by ducks and geese in urban and natural habitats
Bibliographic record
Abstract
Abstract Ducks and geese are little studied dispersal vectors for plants lacking a fleshy fruit, and our understanding of the traits associated with these plants is limited. We analyzed 507 faecal samples of mallard ( Anas platyrhynchos ) and Canada goose ( Branta canadensis ) from 18 natural and urban wetlands in England, where they are the dominant resident waterfowl. We recovered 930 plant diaspores from 39 taxa representing 18 families, including 28 terrestrial and five aquatic species and four aliens. Mallards had more seeds and seed species per sample than geese, more seeds from barochory and hydrochory syndromes, and seeds that on average were larger and from plants with greater moisture requirements (i.e., more aquatic). Mallards dispersed more plant species than geese in natural habitats. Plant communities and traits dispersed were different between urban (e.g., more achenes) and natural (e.g., more capsules) habitats. Waterfowl can readily spread alien species from urban into natural environments but also allow native terrestrial and aquatic plants to disperse in response to climate heating or other global change. Throughout the temperate regions of the Northern Hemisphere, the mallard is accompanied by a goose (either the Canada goose or the greylag goose) as the most abundant waterfowl in urbanized areas. This combination provides a previously overlooked seed dispersal service for plants with diverse traits.
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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 | 0.000 | 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".