Habitat change at a multi-species goose breeding area on Southampton Island, Nunavut, Canada, 1979–2010
Bibliographic record
Abstract
Foraging by hyperabundant Arctic-nesting geese has significant impacts on vegetation of Arctic and subarctic coastal lowlands, but long-term data sets documenting these changes are rare. We undertook intensive surveys of plant communities at East Bay and South Bay, Southampton Island, Nunavut, Canada, in July 2010. Lesser Snow Geese, Ross’s Geese, Cackling Geese, and Brant nest and rear young at these sites; the first three have experienced up to 10-fold increases since the 1970s. At East Bay, we found significant declines in graminoids over the 31-year span, as well as significant declines in lichen and willow cover, and significant increases in rock cover. Transect data indicated graminoids were present at only 15%–36% of points at East Bay, whereas at South Bay, graminoids were present at 28%–90% of points. Moss was more prominent in transects at South Bay than at East Bay (40%–85% vs. 19%–42%), but quadrat data indicated much more of the moss cover at South Bay apparently was dead than at East Bay. Puccinellia phryganodes (Trin.) Scribn. & Merr. exceeded 1% in only two transects. Our data demonstrate a striking decline of preferred forage species and increases in non-forage cover, consistent with the hypothesis that changes resulted from persistent long-term foraging by the four species of breeding geese between spring arrival and late summer departure.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".