Seaduck engineers in the Arctic Archipelago: nesting eiders deliver marine nutrients and transform the chemistry of island soils, plants, and ponds
Bibliographic record
Abstract
Abstract Seabirds are thought to provide ecological services such as the movement of nutrients between marine and terrestrial ecosystems, which may be especially critical to productivity and diversity in nutrient-poor environments. Most Arctic ecosystems are unaffected by local human impacts and are naturally nutrient poor and especially sensitive to warming. Here, we assessed the effects of nesting common eider ducks ( Somateria mollissima ) on soil, vegetation, and pond sediments on island archipelagoes in Hudson Strait between Nunavut and Québec, Canada. Soil, moss, and pond sediments were significantly higher in nitrogen on islands with large numbers of nesting eiders compared to sites with no nesting birds. The highest concentrations of nitrogen in soils and moss occurred at the margins of ponds on eider islands, which correspond to the areas of highest eider use. δ 15 N and δ 34 S values in soils, moss, and sediments indicated substantial marine-derived organic matter inputs at the higher nutrient sites. We propose that by foraging on coastal marine benthic invertebrates and returning to islands to nest, eider ducks bio-transport and concentrate marine-derived nutrients to their colony islands, fertilizing Arctic island ecosystems in the process. As common eiders nest on thousands of low to mid-latitude islands throughout the circumpolar Arctic, these nutrient inputs likely dramatically affect biota and ecosystem functioning throughout the tundra biome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".