Inter-colony isotopic niche dynamics and the effects of cumulative stressors on incubation phenology and behaviour in an Arctic seabird
Bibliographic record
Abstract
Human activity has resulted in global environmental shifts that are altering Arctic marine systems through rising air and ocean temperatures, and a dramatic reduction of sea ice. These changes influence food web dynamics through changes in primary producer abundance and distribution, such as ice algae and phytoplankton, as well as wildlife at higher trophic levels. Mercury is an endocrine-disrupting metal elevated in the environment due to human industrial activity. Mercury accumulation is influenced by prey choice, and therefore is affected by altered food web dynamics. Elevated mercury has been shown to impact incubation behaviour and decrease reproductive success in birds. Worse yet, this effect may be amplified by concurrent exposure to elevated air temperatures, however, these relationships have not been empirically researched to date. We first examined the impact of foraging behaviour on mercury exposure by examining the multidimensional isotopic niche of ten common eider (Somateria mollissima, Mitiq) colonies. Results suggest a wide degree of variation in their foraging strategies determined via stable isotope analysis, potentially impacted by changes in primary production, sea ice presence and migratory status. We then examined whether variation in the multiple stressors, mercury and environmental conditions, affected incubation phenology and behaviour. We found that exposure to higher temperatures during incubation, both individually and simultaneously with elevated mercury, predicted an increase in movement during incubation. Shorter incubation durations also occurred in birds exposed to high air temperatures, resulting in a decreased likelihood of nest success. For the first time, our results suggest that eider colonies across the Arctic have a wide degree of variation in their foraging strategies which influence mercury levels. Individuals with elevated mercury, when combined with elevated air temperatures, were shown to have potential implications on incubation behaviour. Thus, exploring multiple stressor effects on seabird physiology and behaviour is important to contribute to our knowledge of anthropogenic effects on ecosystems, and potential means of effective conservation of impacted seabird populations.
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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) | 0.000 | 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".