The fate of mercury in Arctic terrestrial and aquatic ecosystems, a review
Bibliographic record
Abstract
Environmental context Mercury, in its methylated form, is a neurotoxin that biomagnifies in marine and terrestrial foodwebs leading to elevated levels in fish and fish-eating mammals worldwide, including at numerous Arctic locations. Elevated mercury concentrations in Arctic country foods present a significant exposure risk to Arctic people. We present a detailed review of the fate of mercury in Arctic terrestrial and marine ecosystems, taking into account the extreme seasonality of Arctic ecosystems and the unique processes associated with sea ice and Arctic hydrology. Abstract This review is the result of a series of multidisciplinary meetings organised by the Arctic Monitoring and Assessment Programme as part of their 2011 Assessment ‘Mercury in the Arctic’. This paper presents the state-of-the-art knowledge on the environmental fate of mercury following its entry into the Arctic by oceanic, atmospheric and terrestrial pathways. Our focus is on the movement, transformation and bioaccumulation of Hg in aquatic (marine and fresh water) and terrestrial ecosystems. The processes most relevant to biological Hg uptake and the potential risk associated with Hg exposure in wildlife are emphasised. We present discussions of the chemical transformations of newly deposited or transported Hg in marine, fresh water and terrestrial environments and of the movement of Hg from air, soil and water environmental compartments into food webs. Methylation, a key process controlling the fate of Hg in most ecosystems, and the role of trophic processes in controlling Hg in higher order animals are also included. Case studies on Eastern Beaufort Sea beluga (Delphinapterus leucas) and landlocked Arctic char (Salvelinus alpinus) are presented as examples of the relationship between ecosystem trophic processes and biologic Hg levels. We examine whether atmospheric mercury depletion events (AMDEs) contribute to increased Hg levels in Arctic biota and provide information on the links between organic carbon and Hg speciation, dynamics and bioavailability. Long-term sequestration of Hg into non-biological archives is also addressed. The review concludes by identifying major knowledge gaps in our understanding, including: (1) the rates of Hg entry into marine and terrestrial ecosystems and the rates of inorganic and MeHg uptake by Arctic microbial and algal communities; (2) the bioavailable fraction of AMDE-related Hg and its rate of accumulation by biota and (3) the fresh water and marine MeHg cycle in the Arctic, especially the marine MeHg cycle.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".