A Review of Circumpolar Arctic Marine Mammal Health—A Call to Action in a Time of Rapid Environmental Change
Bibliographic record
Abstract
The impacts of climate change on the health of marine mammals are increasingly being recognised. Given the rapid rate of environmental change in the Arctic, the potential ramifications on the health of marine mammals in this region are a particular concern. There are eleven endemic Arctic marine mammal species (AMMs) comprising three cetaceans, seven pinnipeds, and the polar bear (Ursus maritimus). All of these species are dependent on sea ice for survival, particularly those requiring ice for breeding. As air and water temperatures increase, additional species previously non-resident in Arctic waters are extending their ranges northward, leading to greater species overlaps and a concomitant increased risk of disease transmission. In this study, we review the literature documenting disease presence in Arctic marine mammals to understand the current causes of morbidity and mortality in these species and forecast future disease issues. Our review highlights potential pathogen occurrence in a changing Arctic environment, discussing surveillance methods for 35 specific pathogens, identifying risk factors associated with these diseases, as well as making recommendations for future monitoring for emerging pathogens. Several of the pathogens discussed have the potential to cause unusual mortality events in AMMs. Brucella, morbillivirus, influenza A virus, and Toxoplasma gondii are all of concern, particularly with the relative naivety of the immune systems of endemic Arctic species. There is a clear need for increased surveillance to understand baseline disease levels and address the gravity of the predicted impacts of climate change on marine mammal species.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".