Arctic climate change and pollution impact little auk foraging and fitness across a decade
Bibliographic record
Abstract
Ongoing global changes apply drastic environmental forcing onto Arctic marine ecosystems, particularly through ocean warming, sea-ice shrinkage and enhanced pollution. To test impacts on arctic marine ecological functioning, we used a 12-year integrative study of little auks (Alle alle), the most abundant seabird in the Atlantic Arctic. We monitored the foraging ecology, reproduction, survival and body condition of breeding birds, and we tested linkages between these biological variables and a set of environmental parameters including sea-ice concentration (SIC) and mercury contamination. Little auks showed substantial plasticity in response to SIC, with deeper and longer dives but less time spent underwater and more time flying when SIC decreased. Their diet also contained less lipid-rich ice-associated prey when SIC decreased. Further, in contrast to former studies conducted at the annual scale, little auk fitness proxies were impacted by environmental changes: Adult body condition and chick growth rate were negatively linked to SIC and mercury contamination. However, no trend was found for adult survival despite high inter-annual variability. Our results suggest that potential benefits of milder climatic conditions in East Greenland may be offset by increasing pollution in the Arctic. Overall, our study stresses the importance of long-term studies integrating ecology and ecotoxicology.
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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 | 0.000 | 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".