Examining the links between environmental variation, foraging behaviour and foraging success in an Arctic seabird
Bibliographic record
Abstract
Arctic regions are experiencing increasing variability in inter-annual sea ice dynamics ultimately impacting marine Arctic ecosystems. Arctic-breeding seabirds, such as thick-billed murres (Uria lomvia) are thus likely to be negatively impacted by fluctuating environmental conditions through its influence on prey availability. Additional extrinsic factors (colony size and chick demand) and intrinsic factors (sex) are also likely to impact foraging behaviour and success of murres in combination with environmental conditions. First, we tested the effect of colony size on colony sensitivity to environmental change at two low Arctic colonies of varying sizes, Coats Island, Nunavut and Digges Island, Nunavut across multiple years (2014–2019) in response to broad-scale and fine-scale environmental conditions. Foraging behaviour and foraging success varied in response to environmental variation at both colonies, suggesting flexibility in responding to environmental variability, however, energetic demand was higher at the large colony suggesting increased sensitivity to future environmental change. Second, we investigated the impacts of broad-scale environmental conditions and fine-scale environmental conditions, adult sex, and chick demand on foraging and diving behaviour and foraging success at Coats Island, Nunavut across multiple years (2017–2019). We observed variation in foraging success across strategies suggesting murres at a population level may buffer environmental change through the use of different strategies. Overall, our results suggest warmer oceanic conditions could be benefitting murres breeding at low Arctic sites, especially at a larger colony that has an increased foraging range.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".