Investigating the dynamics of methylmercury bioaccumulation in the Beaufort Sea shelf food web: a modeling perspective
Bibliographic record
Abstract
High levels of methylmercury (MeHg) have been reported in Arctic marine biota, posing health risks to wildlife and human beings. Although MeHg concentrations of some Arctic species have been monitored for decades, the key environmental and ecological factors driving temporal trends of MeHg are largely unclear. We develop an ecosystem-based MeHg bioaccumulation model for the Beaufort Sea shelf (BSS) using the Ecotracer module of Ecopath with Ecosim, and apply the model to explore how MeHg toxicokinetics and food web trophodynamics affect bioaccumulation in the BSS food web. We show that a food web model with complex trophodynamics and relatively simple MeHg model parametrization can capture the observed biomagnification pattern of the BSS. While both benthic and pelagic production are important for transferring MeHg to fish and marine mammals, simulations suggest that benthic organisms are primarily responsible for driving the high trophic magnification factor in the BSS. We illustrate ways of combining empirical observations and modelling experiments to generate hypotheses about factors affecting food web bioaccumulation, including the MeHg elimination rate, trophodynamics, and species migration behavior. The results indicate that population dynamics rather than MeHg elimination may determine population-wide concentrations for fish and lower trophic level organisms, and cause large differences in concentrations between species at similar trophic levels. This research presents a new tool and lays the groundwork for future research to assess the pathways of global environmental changes in MeHg bioaccumulation in Arctic ecosystems in the past and the future.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".