A unifying framework for the transient parasite dynamics of migratory hosts
Bibliographic record
Abstract
Migrations allow animals to track seasonal changes in resources, find mates, and avoid harsh climates, but these regular, long-distance movements also have implications for parasite dynamics and animal health. Migratory animals have been dubbed "superspreaders" of infection, but migration can also reduce parasite burdens within host populations via migratory escape from contaminated habitats and transmission hotspots, migratory recovery due to parasite mortality, and migratory culling of infected individuals. Here, we show that a single migratory host-macroparasite model can give rise to these different phenomena under different parametrizations, providing a unifying framework for a mechanistic understanding of the parasite dynamics of migratory animals. Importantly, our model includes the impact of parasite burden on host movement capability during migration, which can lead to "parasite-induced migratory stalling" due to a positive feedback between increasing parasite burdens and reduced movement. Our results provide general insight into the conditions leading to different health outcomes in migratory wildlife. Our approach lays the foundation for tactical models that can help understand, predict, and mitigate future changes of disease risk in migratory wildlife that may arise from shifting migratory patterns, loss of migratory behavior, or climate effects on parasite development, mortality, and transmission.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".