Coastal foraging increases mercury concentrations in a breeding seabird: Insights from isotopes, biologging, and prey
Bibliographic record
Abstract
Mercury concentrations can vary substantially across spatial and temporal scales. As mobile marine predators, seabirds offer a unique opportunity to directly link foraging tactics with mercury burdens, because temporal variation in mercury can be related to spatial variation in foraging. Breeding razorbills ( Alca torda ) forage in various habitats around colonies, which can affect their mercury burdens. Here, we explore how mercury concentrations in red blood cells are influenced by foraging tactics (movement and trophic ecology) using GPS tracking and stable isotope dietary analysis (δ 15 N, δ 13 C, and δ 34 S), as well as by environmental signals, assessed through prey observations using nest-based cameras in two years (2021 and 2022) in breeding razorbills in the Gulf of St-Lawrence, Canada. Total mercury levels varied significantly between years, and all individuals exceeded low-risk toxicity thresholds. In the higher mercury year, razorbills foraged closer to the coast and had lower δ 13 C and δ 34 S values, suggesting a shift in foraging tactics. Although prey species composition did not change between years, individuals in the high mercury year brought back smaller prey and more items per load. These findings suggest that elevated mercury concentrations in razorbills may be linked to foraging in more coastal areas. Thus, small-scale changes, such as a shift to inshore coastal foraging, may expose seabirds to cumulative freshwater/terrestrial inputs and potentially higher mercury concentrations in prey. By investigating ecotoxicological risks associated with foraging-related contamination using multiple simultaneous approaches, our study provides insights into how feeding tactics can drive mercury contamination in sympatric seabirds foraging in coastal environments. • We compared red blood cell Hg in chick-rearing razorbills over two breeding seasons. • Hg levels were ~ 1.5 times higher in a year when individuals foraged closer to shore. • Hg levels were higher in a year with lower δ 13 C and δ 34 S values and smaller prey items. • Between-year shifts towards the coast with more terrestrial inputs may raise Hg burdens. • Hg exceeded reported low-risk toxicity thresholds in both years.
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 | 0.000 |
| 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".