Ecological factors alter how spatial overlap predicts viral infection dynamics in wild rodent populations
Bibliographic record
Abstract
Spatial overlap between animals in wildlife populations can have important implications for pathogen transmission. Ecological factors and animal demographic traits can influence animal space use and spatial overlap, but it is unclear how these interactions drive pathogen transmission. We experimentally manipulated wild bank vole populations via resource supplementation and anthelmintic treatment. Using network analysis, we investigated the relationship between spatial overlap and infection likelihood of an endemic zoonotic hantavirus, including how vole sex and reproductive status interact with spatial behaviour to affect infection likelihood. Spatial overlap in a previous month drove the likelihood of current hantavirus infection, and food supplementation and anthelmintic treatment altered the effects of spatial overlap on infection likelihood. Vole sex and reproductive status were important factors determining whether spatial overlap increased or decreased the likelihood of hantavirus infection and interacted with resource supplementation and anthelmintic treatment, generating different infection dynamics in each treatment. Our research provides rare empirical evidence linking previous spatial overlap to current infection status in wildlife populations, with implications for understanding disease dynamics and persistence as well as developing effective management efforts. We further highlight the importance of incorporating variation in ecological factors and host demography when studying pathogen transmission in wildlife systems.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".