Long‐term increases in pathogen seroprevalence in polar bears (<i>Ursus maritimus</i>) influenced by climate change
Bibliographic record
Abstract
The influence of climate change on wildlife disease dynamics is a burgeoning conservation and human health issue, but few long-term studies empirically link climate to pathogen prevalence. Polar bears (Ursus maritimus) are vulnerable to the negative impacts of sea ice loss as a result of accelerated Arctic warming. While studies have associated changes in polar bear body condition, reproductive output, survival, and abundance to reductions in sea ice, no long-term studies have documented the impact of climate change on pathogen exposure. We examined 425 serum samples from 381 adult polar bears, collected in western Hudson Bay (WH), Canada, for antibodies to selected pathogens across three time periods: 1986-1989 (n = 157), 1995-1998 (n = 159) and 2015-2017 (n = 109). We ran serological assays for antibodies to seven pathogens: Toxoplasma gondii, Neospora caninum, Trichinella spp., Francisella tularensis, Bordetella bronchiseptica, canine morbillivirus (CDV) and canine parvovirus (CPV). Seroprevalence of zoonotic parasites (T. gondii, Trichinella spp.) and bacterial pathogens (F. tularensis, B. bronchiseptica) increased significantly between 1986-1989 and 1995-1998, ranging from +6.2% to +20.8%, with T. gondii continuing to increase into 2015-2017 (+25.8% overall). Seroprevalence of viral pathogens (CDV, CPV) and N. caninum did not change with time. Toxoplasma gondii seroprevalence was higher following wetter summers, while seroprevalences of Trichinella spp. and B. bronchiseptica were positively correlated with hotter summers. Seroprevalence of antibodies to F. tularensis increased following years polar bears spent more days on land, and polar bears previously captured in human settlements were more likely to be seropositive for Trichinella spp. As the Arctic has warmed due to climate change, zoonotic pathogen exposure in WH polar bears has increased, driven by numerous altered ecosystem pathways.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".