Multiple scales of selection influence the evolutionary emergence of novel pathogens

Author:

Park Miran1,Loverdo Claude1,Schreiber Sebastian J.2,Lloyd-Smith James O.13

Affiliation:

1. Department of Ecology and Evolutionary Biology, University of California, 610 Charles E. Young Dr. South, Los Angeles, CA 90095, USA

2. Department of Evolution and Ecology and Graduate Group in Applied Mathematics, University of California, Davis, CA 95616, USA

3. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA

Abstract

When pathogens encounter a novel environment, such as a new host species or treatment with an antimicrobial drug, their fitness may be reduced so that adaptation is necessary to avoid extinction. Evolutionary emergence is the process by which new pathogen strains arise in response to such selective pressures. Theoretical studies over the last decade have clarified some determinants of emergence risk, but have neglected the influence of fitness on evolutionary rates and have not accounted for the multiple scales at which pathogens must compete successfully. We present a cross-scale theory for evolutionary emergence, which embeds a mechanistic model of within-host selection into a stochastic model for emergence at the population scale. We explore how fitness landscapes at within-host and between-host scales can interact to influence the probability that a pathogen lineage will emerge successfully. Results show that positive correlations between fitnesses across scales can greatly facilitate emergence, while cross-scale conflicts in selection can lead to evolutionary dead ends. The local genotype space of the initial strain of a pathogen can have disproportionate influence on emergence probability. Our cross-scale model represents a step towards integrating laboratory experiments with field surveillance data to create a rational framework to assess emergence risk.

Publisher

The Royal Society

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology

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