Diet of Mute Swans in Lower Great Lakes Coastal Marshes
Bibliographic record
Abstract
Abstract: During the past 30 years, nonnative mute swan ( Cygnus olor ) populations have greatly increased, and continue to increase, in the eastern United States and within the lower Great Lakes (LGL) region. As a result, there is much concern regarding impacts of mute swan on native waterfowl, aquatic plants, and marsh habitats. There are presently only limited dietary data for mute swans in North America and none exist for birds in the LGL region. Thus, in 2001, 2002, and 2004 we collected 132 mute swans from LGL coastal marshes in Ontario, Canada, to determine dietary composition and to evaluate 1) seasonal and sex‐related variation in adult diets and 2) age‐related dietary differences. Adult diets did not differ among years, collection sites, or seasons, but female diets contained more pondweed spp. ( Potamogeton spp.) and less slender naiad ( Najas flexilis ) and common waterweed ( Elodea canadensis ) than did diets of males. Adult males, adult females, and cygnets had similar diets during summer and autumn. Overall, mute swan diets mainly consisted of above‐ground biomass of pondweed spp., muskgrass ( Chara vulgaris ), coontail ( Ceratophyllum demersum ), slender naiad, common waterweed, wild celery ( Vallisneria americana ), and wild rice ( Zizania palustris ); below‐ground parts of wild celery, sago pondweed ( Stuckenia pectinatus ), and arrowhead spp. ( Sagittaria spp.) were eaten infrequently. Comparison of our findings with those of other diet studies suggested considerable dietary overlap between mute swans and several other species of native waterfowl. Thus, we suggest that mute swans have potential to compete with native waterfowl and impact aquatic plants that are important waterfowl foods within LGL coastal marshes. Further, our results can be used to assess which aquatic plant species may be most impacted by foraging activities of mute swans at other important waterfowl stopover and wintering sites in North America. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):726–732; 2008)
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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 | 0.000 |
| 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".