ArticleCommunications biology2025
A phylogenetic epidemiology approach to predicting the establishment of multi-host plant pests.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- On Collaborative Governance: Building Consensus on Priorities to Manage Invasive Species Through Collective Action.Plant-environment interactions (Hoboken, N.J.) · 2025Article
- A phylogenetic epidemiology approach to predicting the establishment of multi-host plant pests.Communications biology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Forecasting emergent pest spread is paramount to mitigating their impacts. For host-specialized pests, epidemiological models of spread through a single host population are well developed. However, most pests attack multiple host species; the challenge is predicting which communities are most vulnerable to infestation. Here, we develop a phylogenetically-informed approach to predict establishment of emergent multi-host pests across heterogeneous landscapes. We model a beetle-pathogen symbiotic complex on trees, introduced from Southeast Asia to California. The phyloEpi model for likelihood of establishment was predicted from the phylogenetic composition of woody species in the invaded community and the influence of temperature on beetle reproduction. Plant communities dominated by close relatives of known epidemiologically critical hosts were four times more likely to become infested than communities with more distantly related species. Where microclimate favored beetle reproduction, pest establishment was greater than expected based only on species composition. We applied this phyloEpi model to predict infestation risk in California using weather data and complete tree inventories from 9262 1-km
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