Climate change increases the distribution of reservoirs of the Raccoon Rabies Virus in Quebec
Bibliographic record
Abstract
Zoonoses often emerge in environments where human-animal interactions intensify. In Québec, climate change and land use alterations are suspected drivers in the shifts of the potential distribution of urban-adapted hosts such as raccoons (Procyon lotor) and striped skunks (Mephitis mephitis), which serve as reservoirs for Raccoon Rabies Virus (RRV; a variant of Lyssavirus rabies). We used species distribution modeling (SDM) to predict current and project future potential habitats for P. lotor and M. mephitis under three climate change scenarios (SSP126, SSP370, SSP585) from 2021 to 2100. Our model predicts significant northward expansions of potential habitats for both species, with P. lotor exhibiting faster and broader shifts compared to M. mephitis. High-emission scenarios further amplify these shifts, proportionally favoring the apparition of favorable habitats for P. lotor. Potential habitats for these reservoirs are projected to overlap with areas that currently lack robust infrastructure for surveillance and vaccination, highlighting potential public health vulnerabilities. Using a technique from interpretable machine learning, we provide a more mechanistic understanding of why the response of both reservoirs to climate change is decoupled. This study underscores the need for proactive monitoring of ecological and epidemiological shifts in Quebec as climate change alters the potential distribution of key wildlife reservoirs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".