Parasite prevalence is determined by infection state- and risk-dependent dispersal of the host
Bibliographic record
Abstract
The spread of parasites and the emergence of disease are currently threatening global biodiversity and human welfare. To address this threat, we need to better understand those factors that determine parasite persistence and prevalence. It is known that dispersal is central to the spatial dynamics of host-parasite systems. Yet past studies have typically assumed that dispersal is a species-level constant, despite a growing body of empirical evidence that dispersal varies with ecological context, including the risk of infection and aspects of host state such as infection status (parasite-dependent dispersal; PDD). Here, we develop a metapopulation model to understand how different forms of PDD shape the prevalence of a directly transmitted parasite. We show that increasing host dispersal rate can increase, decrease or cause a non-monotonic change in regional parasite prevalence, depending on the type of PDD and characteristics of the host-parasite system (transmission rate, virulence, and dispersal mortality). This result contrasts with previous studies with parasite-independent dispersal which concluded that prevalence increases with host dispersal rate. We argue that accounting for host dispersal responses to parasites is necessary for a complete understanding of host-parasite dynamics and for predicting how parasite prevalence will respond to changes such as human alteration of landscape connectivity. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".