A review of ecological impacts of global climate change on persistent organic pollutant and mercury pathways and exposures in arctic marine ecosystems
Bibliographic record
Abstract
Abstract Bioaccumulative and biomagnifying contaminants, such as persistent organic pollutants (POPs) and mercury (Hg), have for decades been recognized as a health concern in arctic marine biota. In recent years, global climate change (GCC) and related loss of arctic sea ice have been observed to be driving substantial change in arctic ecosystems. This review summarizes findings documenting empirical links between GCC-induced ecological changes and alterations in POP and Hg exposures and pathways in arctic marine ecosystems. Most of the studies have reported changes in POP or Hg concentrations in tissue in relation to GCC-induced changes in species trophic interactions. These studies have typically focused on the role of changes in abundance, habitat range or accessibility of prey species, particularly in relation to sea ice changes. Yet, the ecological change that resulted in contaminant trend changes has often been unclear or assumed. Other studies have successfully used chemical tracers, such as stable nitrogen and carbon isotope ratios and fatty acid signatures to link such ecological changes to contaminant level variations or trends. Lower sea ice linked-diet changes/variation were associated with higher contaminant levels in some populations of polar bears, ringed seals, and thick-billed murres, but the influence of changing trophic interactions on POP levels and trends varied widely in both magnitude and direction. We suggest that future research in this new area of GCC-linked ecotoxicology should focus on routine analysis of ancillary ecological metrics with POP and Hg studies, simultaneous consideration of the multiple mechanisms by which GCC and contaminant interactions can occur, and targeted research on changing exposures and toxicological effects in species known to be sensitive to both GCC and contaminants.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".