Exploring Drivers of Historic Mercury Trends in Beluga Whales Using an Ecosystem Modeling Approach
Bibliographic record
Abstract
While mercury occurs naturally in the environment, human activity has significantly disturbed its biogeochemical cycle. Inorganic mercury entering aquatic systems can be transformed into methylmercury, a strong neurotoxicant that builds up in organisms and affects ecosystem and public health. In the Arctic, top predators such as beluga whales, an ecologically and culturally significant species for many Inuit communities, can contain high concentrations of methylmercury. Historical mercury concentrations in beluga in the western Canadian Arctic's Beaufort Sea cannot be explained by mercury emission trends alone; in addition, they could potentially be driven by climate change impacts, such as rising temperatures and sea ice melt. These changes can affect mercury bioaccumulation through different pathways, including ecological and mercury transport processes. In this study, we explore key drivers of mercury bioaccumulation in the Beaufort Sea beluga population using Ecopath with Ecosim, an ecosystem modeling approach, and scenarios of environmental change informed by Western Science and Inuvialuit Knowledge. Comparing the effect of historical sea ice cover, sea surface temperature, and freshwater discharge time series, modeling suggests that the timing of historical increases and decreases in beluga methylmercury concentrations can be better explained by the resulting changes to ecosystem productivity rather than by those to mercury inputs and that all three environmental drivers could partially explain the decrease in mercury concentrations in beluga after the mid-1990s. This work highlights the value of multiple knowledge systems and exploratory modeling methods in understanding environmental change and contaminant cycling. Future work building on this research could inform climate change adaptation efforts and inform management decisions in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".