Range area and the extremes of the fast-slow continuum predict pathogen richness in pantropical mammals
Bibliographic record
Abstract
Abstract Surveillance of pathogen richness in wildlife is needed to identify host species with high zoonotic spillover risk. Many predictors of pathogen richness in wildlife hosts have been proposed, but these predictors have mostly been examined separately and not at the pantropical level. Here we analyzed 15 proposed predictors of pathogen richness using a model ensemble composed of bagged random forests, boosted regression trees, and zero-inflated negative binomial mixed-effects models to identify predictors of pathogen richness in wild tropical mammal species. After controlling for research effort, species geographic range area was identified to be the most important predictor by the model ensemble while the most important anthropogenic factor was hunting pressure. Both fast-lived and slow-lived species had greater pathogen richness, showing a non-linear relationship between the species fast-slow continuum of life history traits and pathogen richness, whereby pathogen richness increases near the extremities. The top species with the highest pathogen richness predicted by our model ensemble are Vulpes vulpes, Mus musculus, Canis lupus, Mustela erminea, and Lutra lutra. Our results can help support evidence-informed pathogen surveillance and disease reservoir management to prevent the emergence of future zoonotic diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".