Host community structure can shape pathogen outbreak dynamics through a phylogenetic dilution effect
Bibliographic record
Abstract
Abstract Biodiversity loss and anthropogenic modifications to species communities are impacting the frequency and magnitude of disease emergence events. These changes may be related through mechanisms in which biodiversity either increases (amplifies) or decreases (dilutes) disease prevalence. Biodiversity effects can be direct, when contacts among competent hosts are replaced by contacts with sink hosts, or indirect through the regulation of host abundances. Here, we introduce a multihost compartmental disease model, weighting host competences by their evolutionary relatedness. Our model simulates host communities with substitutive and additive assembly patterns and frequency‐ and density‐dependent pathogen transmission modes, from which we estimate the community disease outbreak potential . Simulations show how differences in phylogenetic structure can switch host communities from diluting to amplifying a disease, even when species richness is unchanged. We additionally show that phylogenetic dilution can occur simultaneously with (classic) amplification through species richness. We illustrate our model using empirical data describing the relationship between phylogenetic distances separating hosts and their likelihood of disease sharing. Our study demonstrates how host evolutionary histories can drive disease dynamics through a phylogenetic dilution effect. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".