Current state of knowledge on biological effects from contaminants on arctic wildlife and fish
Bibliographic record
Abstract
Since the last Arctic Monitoring and Assessment Programme (AMAP) effort to review biological effects of the exposure to organohalogen compounds (OHCs) in Arctic biota, there has been a considerable number of new Arctic effect studies. Here, we provide an update on the state of the knowledge of OHC, and also include mercury, exposure and/or associated effects in key Arctic marine and terrestrial mammal and bird species as well as in fish by reviewing the literature published since the last AMAP assessment in 2010. We aimed at updating the knowledge of how single but also combined health effects are or can be associated to the exposure to single compounds or mixtures of OHCs. We also focussed on assessing both potential individual as well as population health impacts using population-specific exposure data post 2000. We have identified quantifiable effects on vitamin metabolism, immune functioning, thyroid and steroid hormone balances, oxidative stress, tissue pathology, and reproduction. As with the previous assessment, a wealth of documentation is available for biological effects in marine mammals and seabirds, and sentinel species such as the sledge dog and Arctic fox, but information for terrestrial vertebrates and fish remain scarce. While hormones and vitamins are thoroughly studied, oxidative stress, immunotoxic and reproductive effects need further investigation. Depending on the species and population, some OHCs and mercury tissue contaminant burdens post 2000 were observed to be high enough to exceed putative risk threshold levels that have been previously estimated for non-target species or populations outside the Arctic. In this assessment, we made use of risk quotient calculations to summarize the cumulative effects of different OHC classes and mercury for which critical body burdens can be estimated for wildlife across the Arctic. As our ultimate goal is to better predict or estimate the effects of OHCs and mercury in Arctic wildlife at the individual, population and ecosystem level, there remain numerous knowledge gaps on the biological effects of exposure in Arctic biota. These knowledge gaps include the establishment of concentration thresholds for individual compounds as well as for realistic cocktail mixtures that in fact indicate biologically relevant, and not statistically determined, health effects for specific species and subpopulations. Finally, we provide future perspectives on understanding Arctic wildlife health using new in vivo, in vitro, and in silico techniques, and provide case studies on multiple stressors to show that future assessments would benefit from significant efforts to integrate human health, wildlife ecology and retrospective and forecasting aspects into assessing the biological effects of OHC and mercury exposure in Arctic wildlife and fish.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".