Landscape features and sea ice influence nesting common eider abundance and dispersion
Bibliographic record
Abstract
Factors that influence individual and colony spacing are still not well understood in many organisms. Common eiders ( Somateria mollissima (L. 1758)) nest on coastal islands and forage in intertidal and shallow subtidal waters. We considered several biotic and abiotic factors, their interactions, and how these might influence the distribution of eider colonies at several spatial scales in Labrador, Canada. At the island level, nest abundance was not related to intertidal prey density. At the 104 km2grid scale, eider nest abundance and the coefficient of dispersion (CD; the variance to mean ratio of colony size or grid cell, where CD indicates population dispersion) were negatively related to the number of islands. Spring ice cover was positively related to the number of islands but was negatively related to eider nest abundance and to CD. Ice cover – abundance and ice cover – CD were significant at two spatial scales (104 and 455 km2, respectively), but other relationships were weaker at the larger spatial scale. We hypothesize that during the spring, archipelagos with many islands trap ice, providing terrestrial predators access to nesting islands by acting as bridges and that increased predation reduces habitat quality causing nesting eiders to disperse. Our findings suggest that eiders respond to landscape features, including ice cover, a feature that is being influenced by climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".