Combining direct and indirect genetic methods to estimate dispersal for informing wildlife disease management decisions
Bibliographic record
Abstract
Epidemiological models are useful tools for management to predict and control wildlife disease outbreaks. Dispersal behaviours of the vector are critical in determining patterns of disease spread, and key variables in epidemiological models, yet they are difficult to measure. Raccoon rabies is enzootic over the eastern seaboard of North America and management actions to control its spread are costly. Understanding dispersal behaviours of raccoons can contribute to refining management protocols to reduce economic impacts. Here, estimates of dispersal were obtained through parentage and spatial genetic analyses of raccoons in two areas at the front of the raccoon rabies epizootic in Ontario; Niagara (N = 296) and St Lawrence (N = 593). Parentage analysis indicated the dispersal distance distribution is highly positively skewed with 85% of raccoons, both male and female, moving < 3 km. The tail of this distribution indicated a small proportion (< 4%) moves more than 20 km. Analysis of spatial genetic structure provided a similar assessment as the spatial genetic correlation coefficient dropped sharply after 1 km. Directionality of dispersal would have important implications for control actions; however, evidence of directional bias was not found. Separating the data into age and sex classes the spatial genetic analyses detected female philopatry. Dispersal distances differed significantly between juveniles and adults, while juveniles in the Niagara region were significantly more related to each other than adults were to each other. Factors that may contribute to these differences include kin association, and spring dispersal. Changes to the timing and area covered by rabies control operations in Ontario are indicated based on these dispersal data.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".