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Updated: Aug 5, 2026

Vector Competence Analyses on Aedes aegypti Mosquitoes using Zika Virus
Published on: May 31, 2020
Viral Infection Risk Driven by Ecological Trait Susceptibility to Habitat Heterogeneity
Adrian D Zamfir1, Bisola M Babalola1, Miguel A Mora1
1Centro de Biotecnología y Genómica de Plantas (CBGP UPM-CSIC/INIA), Universidad Politécnica de Madrid and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Madrid, Spain.
Pathogen emergence is influenced by host exploitation in diverse environments. Understanding ecological strategies and evolutionary history is key to predicting infection risk and disease spread.
Area of Science:
- Ecology
- Evolutionary Biology
- Plant Pathology
Background:
- Disease transmission and pathogen emergence are driven by host exploitation traits in heterogeneous environments.
- Accurate forecasting of infection risk requires understanding pathogen resource use across diverse host communities.
Purpose of the Study:
- To analyze virus host use by examining incidence responses to habitat heterogeneity for 18 plant viruses.
- To compare evolutionary histories and transmission modes as drivers of infection risk across different habitats.
Main Methods:
- Estimated virus incidence responses to habitat heterogeneity.
- Analyzed 18 plant viruses with varying horizontal transmission modes.
- Compared responses across four habitats with contrasting anthropogenic disturbance levels.
Main Results:
- Plant community composition variation across habitats significantly shaped virus incidence responses.
- Virus transmission mode and evolutionary history, alongside ecological drivers, influenced incidence responses to habitat heterogeneity.
- Both ecological and evolutionary factors determine pathogen host exploitation.
Conclusions:
- Habitat heterogeneity and plant community composition are critical in shaping virus incidence.
- Understanding the interplay of ecological and evolutionary factors is essential for predicting pathogen emergence and infection risk.
- This study provides a foundation for improved forecasting of disease dynamics.
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