Loss of migratory behaviour increases infection risk for a butterfly host
Bibliographic record
Abstract
Long-distance animal migrations have important consequences for infectious disease dynamics. In some cases, migration lowers pathogen transmission by removing infected individuals during strenuous journeys and allowing animals to periodically escape contaminated habitats. Human activities are now causing some migratory animals to travel shorter distances or form sedentary (non-migratory) populations. We focused on North American monarch butterflies and a specialist protozoan parasite to investigate how the loss of migratory behaviours affects pathogen spread and evolution. Each autumn, monarchs migrate from breeding grounds in the eastern US and Canada to wintering sites in central Mexico. However, some monarchs have become non-migratory and breed year-round on exotic milkweed in the southern US. We used field sampling, citizen science data and experimental inoculations to quantify infection prevalence and parasite virulence among migratory and sedentary populations. Infection prevalence was markedly higher among sedentary monarchs compared with migratory monarchs, indicating that diminished migration increases infection risk. Virulence differed among parasite strains but was similar between migratory and sedentary populations, potentially owing to high gene flow or insufficient time for evolutionary divergence. More broadly, our findings suggest that human activities that alter animal migrations can influence pathogen dynamics, with implications for wildlife conservation and future disease risks.
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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) | 0.002 | 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".